How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

•

15 gostaram•8,120 visualizações

This document summarizes a presentation about utilizing MLFlow and Kubernetes to build an enterprise machine learning platform. It discusses challenges that motivated building such a platform, like lack of model management and difficult deployments. The solution presented abstracts data pipelines into modular components to standardize workflows. It also uses MLFlow to package and track models and experiments, and Kubernetes with Kubeflow to deploy models at scale. A demo shows implementing model serving with these tools.

Dados e análise

WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

Nick Pinckernell, Comcast Applied AI Research
Utilizing MLFlow and
Kubernetes to build an
Enterprise ML Platform
#UnifiedAnalytics #SparkAISummit

Topics
TOPIC WHY?
Example of data pipeline abstraction Modular components and reuse are
important for abstracting complex
systems
Ways to package and track ML project
and experiments
Consistency and reproducibility is key
for scale
How Comcast uses Kubeflow to serve
and deploy models and pipelines
A tangible example to help you
brainstorm about your organizations
requirements
3#UnifiedAnalytics #SparkAISummit

Challenges and motivations
• Before, there was no
– model management or tracking
– standardization for model packaging or deployments
• Cumbersome deployment process
– Deployment required code rewrite from research to operations
– Days or weeks to deploy
• Response and tradeoff: restrict model complexity
4#UnifiedAnalytics #SparkAISummit

Requirements
Minimum requirements from our organization
• Zero code refactoring or rewriting between research ready models and
production
• Easier experiment and model tracking
• Researchers need to deploy their own models
• A/B testing for quick model enhancement testing in production
• Ability to modularize and inject custom metrics and workflows at each step
5#UnifiedAnalytics #SparkAISummit

Solution – existing technologies
6#UnifiedAnalytics #SparkAISummit
Research
Model serving
Images,
containers

Data pipeline abstraction
• Determine use cases
• Identify commonalities for modularization
• Abstract interfaces
• Automate configuration
7#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
8#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
9#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
10#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
11#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
12#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
13#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
14#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
15#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
16#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
17#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
18#UnifiedAnalytics #SparkAISummit

Pipeline abstraction
19#UnifiedAnalytics #SparkAISummit

Seldon inference graphs
Allows for complex graphs
• A/B testing
• Ensembles
• Multi-armed bandit
• Custom combinations
20#UnifiedAnalytics #SparkAISummit
https://github.com/SeldonIO/seldon-core/blob/release-0.2/notebooks/advanced_graphs.ipynb

Packaging and tracking
1. Researchers code and train models with Databricks, Spark
2. Experiments tracked with MLFlow
3. Packaging and model tracking with MLFlow and Kubeflow
• MLFLow standard packaging formats
• scikit-learn
• h2o
• TensorFlow
• more
21#UnifiedAnalytics #SparkAISummit

An MLFlow experiment
22#UnifiedAnalytics #SparkAISummit

MLFlow – multiple experiments
23#UnifiedAnalytics #SparkAISummit

MLFlow – multiple experiments
24#UnifiedAnalytics #SparkAISummit

MLFlow packaging
25#UnifiedAnalytics #SparkAISummit

Research and model flow
26#UnifiedAnalytics #SparkAISummit

Research and model flow
27#UnifiedAnalytics #SparkAISummit

Research and model flow – at scale
28#UnifiedAnalytics #SparkAISummit

Model serving with Kubeflow
Considerations and requirements
• Resilient
• Highly available
• Rate limiting
• Shadow deployments
• Auto-scaling (WIP)
29#UnifiedAnalytics #SparkAISummit
Ambassador
http://www.getambassador.io

Throughput
Static number of replicas Determined after
• Constant and burst load testing with Locust
30#UnifiedAnalytics #SparkAISummit

DEMO
A demonstration of
• MLFlow experiments
– Serving the chosen model
• Implementation of components
– Consumer pod
– Model pod
– Producer logic (to simulate real requests)
31#UnifiedAnalytics #SparkAISummit

Choosing the run
32#UnifiedAnalytics #SparkAISummit

Choosing the model
33#UnifiedAnalytics #SparkAISummit

Implementing the model
34#UnifiedAnalytics #SparkAISummit

Implementing the consumer
35#UnifiedAnalytics #SparkAISummit

Implementing the producer
36#UnifiedAnalytics #SparkAISummit

Deploy the model
• Define the YAML / JSON Seldon deployment
• Build the image
– s2i build -E environment_rest .
seldonio/seldon-core-s2i-python3:0.6-SNAPSHOT
sklearn-iris-mlflow:0.3
• Deploy
– kubectl create -f sklearn_iris_deployment.json
-n kubeflow
37#UnifiedAnalytics #SparkAISummit

38#UnifiedAnalytics #SparkAISummit
Grafana metrics

39#UnifiedAnalytics #SparkAISummit
COMCAST IS HIRING
PHILADELPHIA
WASHINGTON, D.C.
SILICON VALLEY
DENVER

DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

Mais conteúdo relacionado

Mais procurados

In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before. The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups. Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.

Using MLOps to Bring ML to Production/The Promise of MLOps

Weaveworks

GSJUG: Mastering Data Streaming Pipelines 09May2023 https://www.meetup.com/futureofdata-princeton/events/293233881/ This is a repost from the Garden State Java Users Group Event. Join me at https://www.meetup.com/garden-state-java-user-group/events/293229660/ See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925 Please note that registration via EventBrite is required to attend either in-person or online. We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting! Abstract: In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors and sinks. We hope to see you on May 9! Speaker Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there, we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors, and sinks. https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178

GSJUG: Mastering Data Streaming Pipelines 09May2023

Timothy Spann

Databricks Overview for MLOps

Databricks

Machine Learning using Kubeflow and Kubernetes

Arun Gupta

Mario Molina, Software Engineer CDC systems are usually used to identify changes in data sources, capture and replicate those changes to other systems. Companies are using CDC to sync data across systems, cloud migration or even applying stream processing, among others. In this presentation we’ll see CDC patterns, how to use it in Apache Kafka, and do a live demo! https://www.meetup.com/Mexico-Kafka/events/277309497/

CDC patterns in Apache Kafka®

confluent

What’s New with Databricks Machine Learning

Databricks

Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.

Seamless End-to-End Production Machine Learning with Seldon and MLflow

Databricks

ML-Ops:Philosophy, Best-Practices and Tools

Jorge Davila-Chacon

Application performance monitoring with Elastic APM and the ELK stack

Alain Lompo

MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.

Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...

confluent

Apply MLOps at Scale by H&M

Databricks

Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure. For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps). Agenda - Data Quality and why it matters - Challenges and solutions of Data Testing - Challenges and solutions of Model Testing - MLOps pipelines and why they matter - How to expand validation pipelines for Data Quality

MLOps and Data Quality: Deploying Reliable ML Models in Production

Provectus

Machine Learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open-source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

"Managing the Complete Machine Learning Lifecycle with MLflow"

Databricks

Join us for an introduction from Idera's CEO Randy Jacops followed by our Keynote Presentation: “The Future of Data is The Data Cloud”; presented by Kent Graziano (AKA The Data Warrior), Chief Technical Evangelist for Snowflake. Lots has happened at Snowflake in the last few years (including a HUGE IPO!). In this session Kent will give an update on Snowflake’s vision of a world with unlimited access to governed data, enabling every organization to tackle the challenges and opportunities of today and be prepared for the possibilities of tomorrow. Every company in the world still struggles with how to take all their siloed data and turn it into insight, quickly. The Snowflake Data Cloud enables organizations, in every industry, to democratize their data and become data-driven. This talk will introduce you to The Data Cloud, how it works, and the problems it solves for real companies across the globe and across industries. Kent will also update you on recent governance innovations such as dynamic data masking, tagging, and row access policies that will help you build a robust and secure analytics platform. About our Keynote Speaker = = = = = = = = = = = = = = = Kent Graziano, is the Chief Technical Evangelist for Snowflake and an award-winning author, speaker, and thought leader. He is an Oracle ACE Director (Alumni), Knight of the OakTable Network, a certified Data Vault Master and Data Vault 2.0 Practitioner (CDVP2), and expert solution architect with over 35 years of experience, including more than 25 years of designing advanced data and analytics architectures (in multiple industries). He is an internationally recognized expert in cloud and agile data design. Mr. Graziano has developed and led many successful software and data analytics implementation teams, including multiple agile DW/BI teams. He has written numerous articles, authored 3 Kindle books, co-authored 4 other books (including the 1st Edition of The Data Model Resource Book), and has given hundreds of presentations around the world.

Idera live 2021: Keynote Presentation The Future of Data is The Data Cloud b...

IDERA Software

MLOps by Sasha Rosenbaum

Sasha Rosenbaum

MLOps with serverless architectures (October 2018)

Julien SIMON

Kafka basics

João Paulo Leonidas Fernandes Dias da Silva

Apache Flink and what it is used for

Aljoscha Krettek

MLops workshop AWS

Gili Nachum

Modern day applications are data driven and data rich. The infrastructure your backends run on are a critical aspect of your environment, and require unique monitoring tools and techniques. In this webinar learn about what DataOps is, and how critical good data ops is to the integrity of your application. Intelligent APM for your data is critical to the success of modern applications. In this webinar you will learn: The power of APM tailored for Data Operations The importance of visibility into your data infrastructure How AIOps makes data ops actionable

Understanding DataOps and Its Impact on Application Quality

DevOps.com

Mais procurados (20)

Using MLOps to Bring ML to Production/The Promise of MLOps

GSJUG: Mastering Data Streaming Pipelines 09May2023

Databricks Overview for MLOps

Machine Learning using Kubeflow and Kubernetes

CDC patterns in Apache Kafka®

What’s New with Databricks Machine Learning

Seamless End-to-End Production Machine Learning with Seldon and MLflow

ML-Ops:Philosophy, Best-Practices and Tools

Application performance monitoring with Elastic APM and the ELK stack

Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...

Apply MLOps at Scale by H&M

MLOps and Data Quality: Deploying Reliable ML Models in Production

"Managing the Complete Machine Learning Lifecycle with MLflow"

Idera live 2021: Keynote Presentation The Future of Data is The Data Cloud b...

MLOps by Sasha Rosenbaum

MLOps with serverless architectures (October 2018)

Kafka basics

Apache Flink and what it is used for

MLops workshop AWS

Understanding DataOps and Its Impact on Application Quality

Semelhante a How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

"We all know the unprecedented potential impact for Machine Learning. But how do you take advantage of the myriad of data and ML tools now available? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios? In this talk, we'll cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular we will show you how to: - Get started quickly using the Databricks Runtime for Machine Learning, that provides pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more. - Get started with most popular Deep Learning frameworks within a few minutes and go deep with state of the art model DL diagnostics tools. - Scale up Deep Learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner with ease. - How all of these ML frameworks get exposed to large and distributed data using Databricks Runtime for Machine Learning."

Accelerating Machine Learning on Databricks Runtime

Databricks

What is Kedro? Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning. Kedro 2-minute Intro Video: https://youtu.be/KEdmJ2ADy_M Kedro Docs: https://kedro.readthedocs.io Kedro GitHub repo: https://github.com/quantumblacklabs/kedro Meetup: https://www.meetup.com/f7324858-b804-4ed8-ba45-580c262189f1/events/280986950/

Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...

Boston Data Engineering

Overview Uber's Michelangelo is a machine learning platform that supports training and serving thousands of models in production. Most Michelangelo customer models are based on Spark Mllib. In this talk, we will describe Michelangelo's experiences with and evolving use of Spark Mllib, particularly in the areas of model persistence and online serving. Extended Description Michelangelo [https://eng.uber.com/michelangelo/] was originally developed to support scalable machine learning for production models. Its end-to-end support for scheduled Spark-based data ingestion and model training, along with model evaluation and deployment for batch and online model serving, has gained wide acceptance across Uber. More recently, Michelangelo is evolving to handle more use cases, including evaluating and serving models trained outside of core Michelangelo, e.g., on a distributed tensorflow platform providing Horovod [https://eng.uber.com/horovod/] or using PySpark in a Jupyter notebook on Data Science Workbench [https://eng.uber.com/dsw/] To support evaluation and serving of models trained outside of Michelangelo, Michelangelo's use of Spark Mllib needed updating, to generalize its mechanisms for model persistence and online serving. In this talk, we will describe these mechanisms and explore possible avenues for open-sourcing them. Speakers: Anne Holler, Michael Mui

Using Spark Mllib Models in a Production Training and Serving Platform: Exper...

Databricks

Collaborative data science workflows have several moving parts, and many organizations struggle with developing an efficient and scalable process. Our solution consists of data scientists individually building and testing Kedro pipelines and measuring performance using MLflow tracking. Once a strong solution is created, the candidate pipeline is trained on cloud-agnostic, GPU-enabled containers. If this pipeline is production worthy, the resulting model is served to a production application through MLflow.

A Collaborative Data Science Development Workflow

Databricks

Machine learning at scale challenges and solutions

Stavros Kontopoulos

Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.

Scaling ML-Based Threat Detection For Production Cyber Attacks

Databricks

Have you ever struggled to smoothly integrate Spark into a production workflow? Spark is an excellent tool for processing data on the terabyte scale, but building a system to move from raw data through featurization, modeling, and prediction serving involves interacting with numerous other components. Over the past year and half my team at ShopRunner has built a production Spark workflow for data science from scratch. In this talk you'll learn about the tools we use, the challenges we encountered, an open-source library we wrote to work through them, and how you can avoid the detours we took along the way. Data science work often begins in an interactive notebook environment, exploring data and testing out different modeling approaches. However, moving towards a production environment means building reproducible workflows, packaging libraries, setting up scheduling and monitoring of jobs, and figuring out ways to serve results to clients in real time. After testing out a variety of tools, we at ShopRunner have settled on a stack including Databricks, Snowflake, Datadog, Jenkins, and S3, ECS, and RDS from the suite of AWS services. These tools each offer unique benefits for their area of focus, but crafting a cohesive pipeline from this range of tools presented a challenge. Come learn how to integrate a Spark workflow into a pipeline that analyzes many terabytes of data, builds machine learning models at scale, and serves predictions to a variety of customer-facing tools. Whether you're just getting started using Spark in productions systems or you already have Spark running in production and want to smooth the process, this talk will leave you better equipped to find and connect the tools that suit your needs.

Connecting the Dots: Integrating Apache Spark into Production Pipelines

Databricks

Part 2/2 (Tech Leaders) Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable challenges. In this talk, we will share common pitfalls, lessons learned, and best practices, while building different enterprise products. In particular, we will focus on the generic use case of ML as the core technology enabling customer-facing products regardless of the specific industry or application. You will: Understand if ML is the right solution for your business and set the right expectations; Deal with the additional uncertainty of ML projects with respect to traditional software; Build a balanced ML team and cover the broad spectrum of skills; Know how to apply the scientific workflow in an agile development framework; Learn how to turn research into production systems including engineering practices and tools; Be able to leverage modern cloud and serverless architecture for scalable, autonomous and cheaper deployments.

Tech leaders guide to effective building of machine learning products

Gianmario Spacagna

In our experience, many problems with production workflows can be traced back to unexpected values in the input data. In a complex pipeline, it can be difficult and costly to trace the root cause of errors. Here we outline our work developing an open source data validation framework built on Apache Spark. Our goal is a tool that easily integrates into existing workflows to automatically make data validation a vital initial step of every production workflow. Our tool is aimed at data scientists and data engineers, who are not necessarily Scala/Python programmers. Our users specify a configuration file that details the data validation checks to be completed. This configuration file is parsed into appropriate queries that are executed with Apache Spark. A status report is logged, which is used to notify developers/maintainers and to establish a historical record of validator checks. This work was inspired by the many great ideas behind Google's TensorFlow Extended (TFX) platform, in particular TensorFlow Data Validation (TFDV). As such we provide optional functionality for our users to visualize their data using Facets Overview and Facets Dive.

Apache Spark Data Validation

Databricks

Working with our customers, developers and partners around the world, it's clear DevOps has become increasingly critical to a team's success. Continuous integration (CI) and continuous delivery (CD) which is part of DevOps, embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. In this session, we will cover how you can automate your entire process from code commit to production using CI/CD pipelines in Azure DevOps for Azure Databricks applications. Using CI/CD practices, you can simplify, speed and improve your cloud development to deliver features to your customers as soon as they're ready.

DevOps for Applications in Azure Databricks: Creating Continuous Integration ...

Databricks

Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.

[DSC Europe 23] Petar Zecevic - ML in Production on Databricks

DataScienceConferenc1

Nasscom ml ops webinar

Sameer Mahajan

Containerized architectures for deep learning

Antje Barth

Legion - AI Runtime Platform

Alexey Kharlamov

AWS CDK is an IaC Framework which enables you to easily create serverless applications and plug together several AWS resources like Lambda, API Gateway and DynamoDB tables with just a few lines of code. But how do I verify if the application works as expected? This talk covers the different ways of testing CDK apps and thereby comprises unit testing Lambda functions, snapshot testing and fine-grained assertion testing of CDK stacks. Since integration testing is an essential part of gaining confidence in your application, we will split an application into several smaller parts, deploy and test them.

Building reliable apps with cdk

RaphaelManke1

Notebooks: they enable our users, but they can cripple our clusters. Let’s fix that. Notebooks have soared in popularity at companies world-wide because they provide an easy, user-friendly way of accessing the cluster-computing power of Spark. But the more users you have hitting a cluster, the harder it is to manage the cluster resources as big, long-running jobs start to starve out small, short-running jobs. While you could have users spin up EMR-style clusters, this reduces the ability to take advantage of the collaborative nature of notebooks. It also quickly becomes expensive as clusters sit idle for long periods of time waiting on single users. What we want is fair, efficient resource utilization on a large single cluster for a large number of users. In this talk we’ll discuss dynamic allocation and the best practices for configuring the current version of Spark as-is to help solve this problem. We’ll also present new improvements we’ve made to address this use case. These include: decommissioning executors without losing cached data, proactively shutting down executors to prevent starvation, and improving the start times of new executors.

Supporting Highly Multitenant Spark Notebook Workloads with Craig Ingram and ...

Spark Summit

AI & Machine Learning Pipelines with Knative

Animesh Singh

Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps. https://dataphoenix.info/the-a-z-of-data/ https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/

The A-Z of Data: Introduction to MLOps

DataPhoenix

Build A Better Way to Deliver IT

Rackspace

Towards Design-space Exploration of Component Chains in Vehicle Software

Alessio Bucaioni

Semelhante a How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform (20)

Accelerating Machine Learning on Databricks Runtime

Boston Data Engineering: Kedro Python Framework for Data Science: Overview an...

Using Spark Mllib Models in a Production Training and Serving Platform: Exper...

A Collaborative Data Science Development Workflow

Machine learning at scale challenges and solutions

Scaling ML-Based Threat Detection For Production Cyber Attacks

Connecting the Dots: Integrating Apache Spark into Production Pipelines

Tech leaders guide to effective building of machine learning products

Apache Spark Data Validation

DevOps for Applications in Azure Databricks: Creating Continuous Integration ...

[DSC Europe 23] Petar Zecevic - ML in Production on Databricks

Nasscom ml ops webinar

Containerized architectures for deep learning

Legion - AI Runtime Platform

Building reliable apps with cdk

Supporting Highly Multitenant Spark Notebook Workloads with Craig Ingram and ...

AI & Machine Learning Pipelines with Knative

The A-Z of Data: Introduction to MLOps

Build A Better Way to Deliver IT

Towards Design-space Exploration of Component Chains in Vehicle Software

Mais de Databricks

DW Migration Webinar-March 2022.pptx

Databricks

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 1

Databricks

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

Data Lakehouse Symposium | Day 4

Databricks

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Democratizing Data Quality Through a Centralized Platform

Databricks

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Learn to Use Databricks for Data Science

Databricks

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Databricks

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Mais de Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Último

Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

(NEHA) Call Girls Katra Call Now: 8617697112 Katra Escorts Booking Contact Details WhatsApp Chat: +91-8617697112 Katra Escort Service includes providing maximum physical satisfaction to their clients as well as engaging conversation that keeps your time enjoyable and entertaining. Plus, they look fabulously elegant, making an impression. Independent Escorts Katra understands the value of confidentiality and discretion; they will go the extra mile to meet your needs. Simply contact them via text messaging or through their online profiles; they'd be more than delighted to accommodate any request or arrange a romantic date or fun-filled night together. We provide:

(NEHA) Call Girls Katra Call Now 8617697112 Katra Escorts 24x7

Call Girls in Nagpur High Profile Call Girls

Just Call Vip call girls roorkee Escorts ☎️9352988975 Two shot with one girl (roorkee ) Booking Contact Details :- WhatsApp Chat :- +91-9352988975 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9352988975 We are available 24*7 all days of the year. Call us — 9352988975 Thank you for Visiting.

Just Call Vip call girls roorkee Escorts ☎️9352988975 Two shot with one girl ...

gajnagarg

➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , India,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...

amitlee9823

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage with sex Booking Contact Details :- WhatsApp Chat :- +91-9920725232 4-May-2024(SMW) Call Girls In Model Towh +91-9920725232 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-9920725232 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9920725232 We are available 24*7 all days of the year. Call us — 9920725232 Thank you for Visiting.

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage...

amitlee9823

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medici

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , India,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...

amitlee9823

Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Valters Lauzums

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

amitlee9823

Anomaly detection and data imputation within time series

Paris Women in Machine Learning and Data Science

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

Just Call Vip call girls Mysore Escorts ☎️9352988975 Two shot with one girl (Mysore ) Booking Contact Details :- WhatsApp Chat :- +91-9352988975 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9352988975 We are available 24*7 all days of the year. Call us — 9352988975 Thank you for Visiting.

Just Call Vip call girls Mysore Escorts ☎️9352988975 Two shot with one girl (...

gajnagarg

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Ban...

amitlee9823

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

amitlee9823

Klinik_ Apotek Onlin 085657271886 Solusi Menggugurkan Masalah Kehamilan Anda Jual Obat Aborsi Asli KLINIK ABORSI TERPEECAYA _ Jual Obat Aborsi Cytotec Misoprostol Asli 100% Ampuh Hanya 3 Jam Langsung Gugur || OBAT PENGGUGUR KANDUNGAN AMPUH MANJUR OBAT ABORSI OLINE" APOTIK Jual Obat Cytotec, Gastrul, Gynecoside Asli Ampuh. JUAL ” Obat Aborsi Tuntas | Obat Aborsi Manjur | Obat Aborsi Ampuh | Obat Penggugur Janin | Obat Pencegah Kehamilan | Obat Pelancar Haid | Obat terlambat Bulan | Ciri Obat Aborsi Asli | Obat Telat Bulan | Pil Aborsi Asli | Cara Menggugurkan Konten | Cara Aborsi Tuntas | Harga Obat Aborsi Asli | Pil Aborsi | Jual Obat Aborsi Cytotec | Cara Aborsi Sendiri | Cara Aborsi Usia 1 Bulan | Cara Aborsi Usia 2 Tahun | Cara Aborsi Usia 3 Bulan | Obat Aborsi Usia 4 Bulan | Cara Abrasi Usia 5 Bulan | Cara Menggugurkan Konten | Kandungan Obat Penggugur | Cara Menghitung Usia Konten | Cara Mengatasi Terlambat Bulan | Penjual Obat Aborsi Asli | Obat Aborsi Garansi | Kandungan Obat Peluntur | Obat Telat Datang Bulan | Obat Telat Haid | Obat Aborsi Paling Murah | Klinik Jual Obat Aborsi | Jual Pil Cytotec | Apotik Jual Obat Aborsi | Kandungan Dokter Abrasi | Cara Aborsi Cepat | Jual Obat Aborsi Bergaransi | Jual Obat Cytotec Asli | Obat Aborsi Aman Manjur | Obat Misoprostol Cytotec Asli. "APA ITU ABORSI" “Aborsi Adalah dengan membendung hormon yang di perlukan untuk mempertahankan kehamilan yaitu hormon progesteron, karena hormon ini dibendung, maka jalur kehamilan mulai membuka dan leher rahim menjadi melunak,sehingga mengeluarkan darah yang merupakan tanda bahwa obat telah bekerja || maksimal 1 jam obat diminum || PENJELASAN OBAT ABORSI USIA 1 _7 BULAN Pada usia kandungan ini, pasien akan merasakan sakit yang sedikit tidak berlebihan || sekitar 1 jam ||. namun hanya akan terjadi pada saatdarah keluar merupakan pertanda menstruasi. Hal ini dikarenakan pada usiakandungan 3 bulan,janin sudah terbentuk sebesar kepalan tangan orang dewasa. Cara kerja obat aborsi : JUAL OBAT ABORSI AMPUH dosis 3 bulan secara umum sama dengan cara kerja || DOSIS OBAT ABORSI 2 bulan”, hanya berbedanya selain mengisolasijanin juga menghancurkan janin dengan formula methotrexate dikandungdidalamnya. Formula methotrexate ini sangat ampuh untuk menghancurkan janinmenjadi serpihan-serpihan kecil akan sangat berguna pada saat dikeluarkan nanti. APA ALASAN WANITA MELAKUKAN ABORSI? Aborsi di lakukan wanita hamil baik yang sudah menikah maupun belum menikah dengan berbagai alasan , akan tetapi alasan yang utama adalah alasan-alasan non medis (termasuk aborsi sendiri / di sengaja/ buatan] MELAYANI PEMESANAN OBAT ABORSI SETIAP HARI, SIAP KIRIM KESELURUH KOTA BESAR DI INDONESIA DAN LUAR NEGERI. HUBUNGI PEMESANAN LEBIH NYAMAN VIA WA/: 085657271886

Jual Obat Aborsi Surabaya ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

ZurliaSoop

Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

➥🔝 7737669865 🔝▻ Sambalpur Call-girls in Women Seeking Men 🔝Sambalpur🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Sambalpur Call-girls in Women Seeking Men 🔝Sambalpur🔝 Esc...

amitlee9823

Ashok Vihar Call Girls in Delhi (–9953330565) Escort Service In Delhi NCR PROVIDE 100% REAL GIRLS ALL ARE GIRLS LOOKING MODELS AND RAM MODELS ALL GIRLS” INDIAN , RUSSIAN ,KASMARI ,PUNJABI HOT GIRLS AND MATURED HOUSE WIFE BOOKING ONLY DECENT GUYS AND GENTLEMAN NO FAKE PERSON FREE HOME SERVICE IN CALL FULL AC ROOM SERVICE IN SOUTH DELHI Ultimate Destination for finding a High Profile Independent Escorts in Delhi.Gurgaon.Noida..!.Like You Feel 100% Real Girl Friend Experience. We are High Class Delhi Escort Agency offering quality services with discretion. We only offer services to gentlemen people. We have lots of girls working with us like students, Russian, models, house wife, and much More We Provide Short Time and Full Night Service Call ☎☎+91–9953330565 ❤꧂ • In Call and Out Call Service in Delhi NCR • 3* 5* 7* Hotels Service in Delhi NCR • 24 Hours Available in Delhi NCR • Indian, Russian, Punjabi, Kashmiri Escorts • Real Models, College Girls, House Wife, Also Available • Short Time and Full Time Service Available • Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24 hours • Daily New Escorts Staff Available • Minimum to Maximum Range Available. Location;- Delhi, Gurgaon, NCR, Noida, and All Over in Delhi Hotel and Home Services HOTEL SERVICE AVAILABLE :-REDDISSON BLU,ITC WELCOM DWARKA,HOTEL-JW MERRIOTT,HOLIDAY INN MAHIPALPUR AIROCTY,CROWNE PLAZA OKHALA,EROSH NEHRU PLACE,SURYAA KALKAJI,CROWEN PLAZA ROHINI,SHERATON PAHARGANJ,THE AMBIENC,VIVANTA,SURAJKUND,ASHOKA CONTINENTAL , LEELA CHANKYAPURI,_ALL 3* 5* 7* STARTS HOTEL SERVICE BOOKING CALL Call WHATSAPP Call ☎+91–9953330565❤꧂ NIGHT SHORT TIME BOTH ARE AVAILABLE

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2. Nick Pinckernell, Comcast Applied AI Research Utilizing MLFlow and Kubernetes to build an Enterprise ML Platform #UnifiedAnalytics #SparkAISummit

3. Topics TOPIC WHY? Example of data pipeline abstraction Modular components and reuse are important for abstracting complex systems Ways to package and track ML project and experiments Consistency and reproducibility is key for scale How Comcast uses Kubeflow to serve and deploy models and pipelines A tangible example to help you brainstorm about your organizations requirements 3#UnifiedAnalytics #SparkAISummit

4. Challenges and motivations • Before, there was no – model management or tracking – standardization for model packaging or deployments • Cumbersome deployment process – Deployment required code rewrite from research to operations – Days or weeks to deploy • Response and tradeoff: restrict model complexity 4#UnifiedAnalytics #SparkAISummit

5. Requirements Minimum requirements from our organization • Zero code refactoring or rewriting between research ready models and production • Easier experiment and model tracking • Researchers need to deploy their own models • A/B testing for quick model enhancement testing in production • Ability to modularize and inject custom metrics and workflows at each step 5#UnifiedAnalytics #SparkAISummit

6. Solution – existing technologies 6#UnifiedAnalytics #SparkAISummit Research Model serving Images, containers

7. Data pipeline abstraction • Determine use cases • Identify commonalities for modularization • Abstract interfaces • Automate configuration 7#UnifiedAnalytics #SparkAISummit

8. Pipeline abstraction 8#UnifiedAnalytics #SparkAISummit

9. Pipeline abstraction 9#UnifiedAnalytics #SparkAISummit

10. Pipeline abstraction 10#UnifiedAnalytics #SparkAISummit

11. Pipeline abstraction 11#UnifiedAnalytics #SparkAISummit

12. Pipeline abstraction 12#UnifiedAnalytics #SparkAISummit

13. Pipeline abstraction 13#UnifiedAnalytics #SparkAISummit

14. Pipeline abstraction 14#UnifiedAnalytics #SparkAISummit

15. Pipeline abstraction 15#UnifiedAnalytics #SparkAISummit

16. Pipeline abstraction 16#UnifiedAnalytics #SparkAISummit

17. Pipeline abstraction 17#UnifiedAnalytics #SparkAISummit

18. Pipeline abstraction 18#UnifiedAnalytics #SparkAISummit

19. Pipeline abstraction 19#UnifiedAnalytics #SparkAISummit

20. Seldon inference graphs Allows for complex graphs • A/B testing • Ensembles • Multi-armed bandit • Custom combinations 20#UnifiedAnalytics #SparkAISummit https://github.com/SeldonIO/seldon-core/blob/release-0.2/notebooks/advanced_graphs.ipynb

21. Packaging and tracking 1. Researchers code and train models with Databricks, Spark 2. Experiments tracked with MLFlow 3. Packaging and model tracking with MLFlow and Kubeflow • MLFLow standard packaging formats • scikit-learn • h2o • TensorFlow • more 21#UnifiedAnalytics #SparkAISummit

22. An MLFlow experiment 22#UnifiedAnalytics #SparkAISummit

23. MLFlow – multiple experiments 23#UnifiedAnalytics #SparkAISummit

24. MLFlow – multiple experiments 24#UnifiedAnalytics #SparkAISummit

25. MLFlow packaging 25#UnifiedAnalytics #SparkAISummit

26. Research and model flow 26#UnifiedAnalytics #SparkAISummit

27. Research and model flow 27#UnifiedAnalytics #SparkAISummit

28. Research and model flow – at scale 28#UnifiedAnalytics #SparkAISummit

29. Model serving with Kubeflow Considerations and requirements • Resilient • Highly available • Rate limiting • Shadow deployments • Auto-scaling (WIP) 29#UnifiedAnalytics #SparkAISummit Ambassador http://www.getambassador.io

30. Throughput Static number of replicas Determined after • Constant and burst load testing with Locust 30#UnifiedAnalytics #SparkAISummit

31. DEMO A demonstration of • MLFlow experiments – Serving the chosen model • Implementation of components – Consumer pod – Model pod – Producer logic (to simulate real requests) 31#UnifiedAnalytics #SparkAISummit

32. Choosing the run 32#UnifiedAnalytics #SparkAISummit

33. Choosing the model 33#UnifiedAnalytics #SparkAISummit

34. Implementing the model 34#UnifiedAnalytics #SparkAISummit

35. Implementing the consumer 35#UnifiedAnalytics #SparkAISummit

36. Implementing the producer 36#UnifiedAnalytics #SparkAISummit

37. Deploy the model • Define the YAML / JSON Seldon deployment • Build the image – s2i build -E environment_rest . seldonio/seldon-core-s2i-python3:0.6-SNAPSHOT sklearn-iris-mlflow:0.3 • Deploy – kubectl create -f sklearn_iris_deployment.json -n kubeflow 37#UnifiedAnalytics #SparkAISummit

38. 38#UnifiedAnalytics #SparkAISummit Grafana metrics

39. 39#UnifiedAnalytics #SparkAISummit COMCAST IS HIRING PHILADELPHIA WASHINGTON, D.C. SILICON VALLEY DENVER

40. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

Semelhante a How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform (20)

Mais de Databricks

Mais de Databricks (20)

Último

Último (20)

How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform