Machine Learning Exchange (MLX)

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Data and AI Assets Catalog and Execution Engine

Machine Learning Exchange (MLX)
Animesh Singh, Christian Kadner, Tommy Li, Andrew Butler

Machine Learning Exchange (MLX) : Data and AI Assets Catalog and Execution Engine

Machine Learning Exchange (MLX)
– Data and AI Assets Catalog and Execution Engine
– Upload, register, execute, and deploy
-AI pipelines and Components
-Models
-Datasets
-Notebooks
– Automated sample pipeline code generation to train, validate, serve your registered models, datasets and notebooks
– Pipelines Engine powered by Kubeflow Pipelines on Tekton, core of Watson Pipelines
– Serving engine by KFServing (Next gen base for WML) , Datasets Management by Dataset Lifecycle Framework
– Preregistered Datasets from Data Asset Exchange (DAX) and Models from Model Asset Exchange
(MAX)
– Model Metadata schema aligned with MLSpec
MLX API Server
Components
Pipelines Notebooks
Object Store
Kubeflow Pipelines on Tekton
MLX UI
SDK
Relational
DB
Models
Datasets
Datashim KFServing
Notebook
Component
- Elyra
MAX/DAX

View, download, and execute Pipelines
4

View, download, and execute Pipeline Components
5

Library of prepackaged models. Register your own
models, run with Pipelines
6

View, modify, and monitor deployed models
7

Register and deploy Datasets (as sharable Volumes for
other assets)
8

Library of prepackaged notebooks. Register your own
notebooks

10
Run Notebooks using Pipelines

Integration

Pluggable Components
Watson
Studio WML
Open
Scale
Kubeflow
Training
Seldon AIF360 ART KATIB KFSERVING
…
…
TASK
STEP
POD
STEP
TASK
STEP STEP
POD
Container Container Container Container
TEKTON
KFP API Server
Components
Pipelines
Object
Store
KFP UI
Relational
DB
COMPILE
KFP SDK
Intermediate
Representation [IR]
Pipelines - default integration with Kubeflow Pipelines and Tekton

Datasets - default integration with Datashim

Models – default integration with KFServing
Kubernetes
Compute cluster
GPU, TPU ,CPU
Model Assets.
Istio
Knative
KFServing
PRE-PROCESS PREDICT POST-PROCESS EXPLAIN

Notebooks – default integration with Elyra Notebook Component

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Amazon Web Services

Comcast is one of the largest cable and telecommunications providers in the country built on decades of mergers, acquisitions, and subscriber growth. The success of our company depends on keeping our customers happy and how quickly we can pivot with changing trends and new technologies. Data abounds within our internal data centers and edge networks as well as both the private and public cloud across multiple vendors. Within such an environment and given such challenges, how do we get AI, machine learning, and data science platforms built so our company can respond to the market, predict our customers’ needs and create new revenue generating products that delight our customers? If you don’t happen to be our friends and colleagues at Google, Facebook, and Amazon, what are technologies, strategies, and toolkits you can employ to bring together disparate data sets and quickly get them into the hands of your data scientists and then into your own production systems for use by your customers and business partners? We’ll explore our journey and evolution and look at specific technologies and decisions that have gotten us to where we are today and demo how our platform works. Speaker Ray Harrison, Comcast, Enterprise Architect Prashant Khanolkar, Comcast, Principal Architect Big Data

A machine learning and data science pipeline for real companies

A machine learning and data science pipeline for real companies

A machine learning and data science pipeline for real companies

DataWorks Summit

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Vertex AI - Unified ML Platform for the entire AI workflow on Google Cloud

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Processing Large Datasets for ADAS Applications using Apache Spark

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Pipelines for model deployment

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(ATS3-APP05) Building Symyx Notebook dashboards with Pipeline Pilot

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Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...

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Machine Learning At Speed: Operationalizing ML For Real-Time Data Streams

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Metadata and Provenance for ML Pipelines with Hopsworks

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Kubeflow Distributed Training and HPO

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With great power comes great responsibility. Adversarial examples in AI pose an asymmetrical challenge with respect to attackers and defenders. AI developers must be empowered to defend deep neural networks against adversarial attacks and allow rapid crafting and analysis of attack and defense methods for machine learning models. Animesh Singh and Tommy Li explain how to implement state-of-the-art methods for attacking and defending classifiers using the open source Adversarial Robustness Toolbox. The library provides AI developers with interfaces that support the composition of comprehensive defense systems using individual methods as building blocks. Animesh and Tommy then demonstrate how to use a Jupyter notebook to leverage attack methods from the Adversarial Robustness Toolbox (ART) into a model training pipeline. This notebook trains a CNN model on the Fashion MNIST dataset, and the generated adversarial samples are used to evaluate the robustness of the trained model.

Defend against adversarial AI using Adversarial Robustness Toolbox

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Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc. In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

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Microservices and containers are now influencing application design and deployment patterns. Sixty percent of all new applications will use cloud-enabled continuous delivery microservice architectures and containers. Service discovery, registration, and routing are fundamental tenets of microservices. Kubernetes provides a platform for running microservices. Kubernetes can be used to automate the deployment of Microservices and leverage features such as Kube-DNS, Config Maps, and Ingress service for managing those microservices. This configuration works fine for deployments up to a certain size. However, with complex deployments consisting of a large fleet of microservices, additional features are required to augment Kubernetes.

Microservices, Kubernetes and Istio - A Great Fit!

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When people aren't talking about VMs and containers, they're talking about serverless architecture. Serverless is about no maintenance. It means you are not worried about low-level infrastructural and operational details. An event-driven serverless platform is a great use case for IoT. In this session at @ThingsExpo, Animesh Singh, an STSM and Lead for IBM Cloud Platform and Infrastructure, detailed how to build a distributed serverless, polyglot, microservices framework using open source technologies like: OpenWhisk: Open source distributed compute service to execute application logic in response to events Docker: To run event driven actions 6. Ansible and BOSH: to deploy the serverless platform MQTT: Messaging protocol for IoT Node-RED: Tool to wire IoT together Consul: Tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable. Kafka: A high-throughput distributed messaging system. StatsD/ELK/Graphite: For statistics, monitoring and logging

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Serverless cloud platforms are a major trend in 2016. Following on from Amazon’s Lambda service, released last year, this year has seen Google, IBM and Microsoft all launch their own solutions. Serverless microservices are executed on-demand, in milliseconds, rather than having to sit idle waiting. Users pay only for the raw computation time used. In this talk detail how to build a distributed serverless, event-driven, microservices framework on OpenStack

How to build an event-driven, polyglot serverless microservices framework on ...

How to build an event-driven, polyglot serverless microservices framework on ...

How to build an event-driven, polyglot serverless microservices framework on ...

According to OpenStack users survey, Cloud Foundry is the 2nd most popular workload on OpenStack. You want to deploy Cloud Foundry on OpenStack or already have. What's next? Cloud Foundry continues to evolve with revolutionary changes, e.g move from bosh-micro to bosh-init, using the new eCPI, move to Diego etc. Same with OpenStack, e.g changes from Keystone v2 to v3, from Liberty to Mitaka, network plugins changes etc. Both IaaS and PaaS layers are changing frequently. How do you do in-place updates/upgrades/operational tasks without impacting user experience at both the layers? In this talk will discuss our lessons learnt operating hybrid Cloud Foundry deployments on top of OpenStack over the last two years and how we used underlying technologies to seamlessly operate them

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OpenStack, Docker, and Cloud Foundry are the three most popular open source projects according to a recent cloud software survey. Docker has taken the cloud world by storm as a revolutionary way to not only run isolated application containers, but also to package them. But how does Docker fit into the paradigm of IaaS and PaaS? More specifically, how does it integrate with OpenStack and Cloud Foundry, the world's most popular infrastructure and platform service implementations? OpenStack, Docker, and Cloud Foundry are the three most popular open source projects according to a recent cloud software survey. Docker has taken the cloud world by storm as a revolutionary way to not only run isolated application containers, but also to package them. But how does Docker fit into the paradigm of IaaS and PaaS? More specifically, how does it integrate with OpenStack and Cloud Foundry, the world's most popular infrastructure and platform service implementations? These charts from our OpenStack Summit talk Vancouver talk how the three leading open source cloud technologies are evolving to work together to support next generation workloads!

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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

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Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

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Machine Learning Exchange (MLX)

1. Machine Learning Exchange (MLX) Animesh Singh, Christian Kadner, Tommy Li, Andrew Butler

2. Machine Learning Exchange (MLX) : Data and AI Assets Catalog and Execution Engine

3. Machine Learning Exchange (MLX) – Data and AI Assets Catalog and Execution Engine – Upload, register, execute, and deploy -AI pipelines and Components -Models -Datasets -Notebooks – Automated sample pipeline code generation to train, validate, serve your registered models, datasets and notebooks – Pipelines Engine powered by Kubeflow Pipelines on Tekton, core of Watson Pipelines – Serving engine by KFServing (Next gen base for WML) , Datasets Management by Dataset Lifecycle Framework – Preregistered Datasets from Data Asset Exchange (DAX) and Models from Model Asset Exchange (MAX) – Model Metadata schema aligned with MLSpec MLX API Server Components Pipelines Notebooks Object Store Kubeflow Pipelines on Tekton MLX UI SDK Relational DB Models Datasets Datashim KFServing Notebook Component - Elyra MAX/DAX

4. View, download, and execute Pipelines 4

5. View, download, and execute Pipeline Components 5

6. Library of prepackaged models. Register your own models, run with Pipelines 6

7. View, modify, and monitor deployed models 7

8. Register and deploy Datasets (as sharable Volumes for other assets) 8

9. Library of prepackaged notebooks. Register your own notebooks

10. 10 Run Notebooks using Pipelines

11. Integration

12. Pluggable Components Watson Studio WML Open Scale Kubeflow Training Seldon AIF360 ART KATIB KFSERVING … … TASK STEP POD STEP TASK STEP STEP POD Container Container Container Container TEKTON KFP API Server Components Pipelines Object Store KFP UI Relational DB COMPILE KFP SDK Intermediate Representation [IR] Pipelines - default integration with Kubeflow Pipelines and Tekton

13. Datasets - default integration with Datashim

14. Models – default integration with KFServing Kubernetes Compute cluster GPU, TPU ,CPU Model Assets. Istio Knative KFServing PRE-PROCESS PREDICT POST-PROCESS EXPLAIN

15. Notebooks – default integration with Elyra Notebook Component