1-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools to a technical audience:
Easier onramp to getting into AI/ML by using GCP AI/ML APIs (Vision, Video Intelligence, Natural Language, Speech-to-Text, Text-to-Speech, Translation) backed by single-task pre-trained models found in Vertex AI, AutoML for finetuning those pre-trained models, and other "friends of AI/ML" Google dev tools & platforms that can help: BigQuery (data warehouse & analysis), Cloud SQL+AlloyDB & Firestore (SQL & NoSQL databases), serverless platforms (App Engine, Cloud Functions, Cloud Run), and introducing the Gemini API (from both Google AI and GCP Vertex AI)
The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
Powerful Google developer tools for immediate impact! (2023-24 A)wesley chun
This is one of two 45-60-min presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
The other version of this talk ("B") focuses more on serverless platforms.
Exploring Google APIs 102: Cloud vs. non-GCP Google APIswesley chun
As a follow-up to his "Exploring Google APIs" talk in 2019 (https://www.youtube.com/watch?v=ri8Bfptgo9Q) on Google APIs and running code on Google Cloud, tech consultant Wesley Chun dives deeper into using the REST APIs available for many Google services, Cloud and otherwise. While developers should expect a common user experience across all Google APIs, this isn't the case, so Wesley, who has spent 13+ years working on different Google API teams, will walk you through the differences you need to know if any of your current or future projects plan on using any Google API, esp. Cloud vs. non-GCP Google APIs. Two of the key topics in this session include an overview of the different client libraries available as well as what's required for authorizing your app's access to Google APIs. Knowledge of accessing APIs from Python or Javascript may be helpful but not necessary.
Build an AI/ML-driven image archive processing workflow: Image archive, analy...wesley chun
Google provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just GWS (Google Workspace) & GCP (Google Cloud) APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from the half-hour presentation about this case study.
The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
Powerful Google developer tools for immediate impact! (2023-24 A)wesley chun
This is one of two 45-60-min presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
The other version of this talk ("B") focuses more on serverless platforms.
Exploring Google APIs 102: Cloud vs. non-GCP Google APIswesley chun
As a follow-up to his "Exploring Google APIs" talk in 2019 (https://www.youtube.com/watch?v=ri8Bfptgo9Q) on Google APIs and running code on Google Cloud, tech consultant Wesley Chun dives deeper into using the REST APIs available for many Google services, Cloud and otherwise. While developers should expect a common user experience across all Google APIs, this isn't the case, so Wesley, who has spent 13+ years working on different Google API teams, will walk you through the differences you need to know if any of your current or future projects plan on using any Google API, esp. Cloud vs. non-GCP Google APIs. Two of the key topics in this session include an overview of the different client libraries available as well as what's required for authorizing your app's access to Google APIs. Knowledge of accessing APIs from Python or Javascript may be helpful but not necessary.
Build an AI/ML-driven image archive processing workflow: Image archive, analy...wesley chun
Google provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just GWS (Google Workspace) & GCP (Google Cloud) APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from the half-hour presentation about this case study.
Image archive, analysis & report generation with Google Cloudwesley chun
Google Cloud provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just Google Workspace & GCP APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from a half- to 1-hour presentation about this case study.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
Cloud computing overview & Technical intro to Google Cloudwesley chun
The document provides an overview of cloud computing and an introduction to Google Cloud. It discusses the different types of cloud services including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It then introduces various Google Cloud Platform (GCP) and G Suite products and services that fall under each category. Examples of code snippets using GCP and G Suite APIs in Python are also provided to demonstrate interacting with these cloud services programmatically.
Exploring Google (Cloud) APIs with Python & JavaScriptwesley chun
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python or JavaScript.
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Exploring Google (Cloud) APIs & Cloud Computing overviewwesley chun
This is a 100-minute tech talk designed for developers to give a comprehensive overview of using Google APIs, primarily those from Google Cloud (G Suite and Google Cloud Platform)
How Google Cloud Platform can help in the classroom/labwesley chun
This is a 90-min tech talk along with hands-on exercises gives a comprehensive, vendor-agnostic overview of cloud computing, primarily targeting educators in the higher education market but is open to any developer. This is followed by an introduction to products in Google Cloud Platform, focusing on its serverless and machine learning products. .
This 2-3 minute presentation is meant to give univeresity hackathoners a brief, high-level overview of Google Cloud and its developer APIs with the purpose of inspiring students to consider these products for their hacks. A longer, more descriptive tech talk comes later.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring 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.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML 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.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
This is an inspirational lightning talk on how developers can take on the future with Google Cloud and other non-Cloud Google tools. It presents various application ideas that are meant to both inspire what's possible as well as show what some of those tools could be.
30-45-min tech talk given at user groups or technical conferences to introducing developers to integrating with Google APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
[Giovanni Galloro] How to use machine learning on Google Cloud PlatformMeetupDataScienceRoma
This document provides an overview of machine learning capabilities on Google Cloud Platform. It discusses how machine learning is used across Google products to improve search ranking and more. It then summarizes the main machine learning capabilities available on GCP, including calling pre-trained models through APIs, building and training custom models on Cloud ML Engine, and using AutoML to build models with little machine learning expertise. The document also briefly introduces upcoming capabilities like Kubeflow for portable machine learning pipelines and AI Hub for discovering and sharing pre-built machine learning solutions.
The document discusses Google Cloud Platform machine learning capabilities for unstructured data like text, speech and images. It introduces the Cloud Vision, Speech and Translate APIs which provide pre-trained machine learning models through REST interfaces to understand unstructured data without requiring ML expertise. Examples are given of using the APIs for tasks like content moderation, sentiment analysis and extracting text/metadata from images.
This document discusses principles for applying continuous delivery practices to machine learning models. It begins with background on the speaker and their company Indix, which builds location and product-aware software using machine learning. The document then outlines four principles for continuous delivery of machine learning: 1) Automating training, evaluation, and prediction pipelines using tools like Go-CD; 2) Using source code and artifact repositories to improve reproducibility; 3) Deploying models as containers for microservices; and 4) Performing A/B testing using request shadowing rather than multi-armed bandits. Examples and diagrams are provided for each principle.
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Power your apps with Gmail, Google Drive, Calendar, Sheets, Slides & morewesley chun
This is a ~90-minute technical introduction to G Suite/Google Apps developer tools, platforms, and APIs. Code samples are in Python+JS. Motivation: encourage developers to write apps integrating with G Suite APIs so they can monetize, taking advantage of the many schools & enterprises that are G Suite users. Delivered sessions at ISTE (Jun 2019), Google Cloud NEXT (Jul 2018), Google Cloud Summit - São Paulo (Nov 2017), DevFest DC (May 2017), DevFest NYC (Nov 2016), and GDG LA DevFest (Dec 2016).
Helixa uses serverless machine learning architectures to power an audience intelligence platform. It ingests large datasets and uses machine learning models to provide insights. Helixa's machine learning system is built on AWS serverless services like Lambda, Glue, Athena and S3. It features a data lake for storage, a feature store for preprocessed data, and uses techniques like map-reduce to parallelize tasks. Helixa aims to build scalable and cost-effective machine learning pipelines without having to manage servers.
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Image archive, analysis & report generation with Google Cloudwesley chun
Google Cloud provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just Google Workspace & GCP APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from a half- to 1-hour presentation about this case study.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
Cloud computing overview & Technical intro to Google Cloudwesley chun
The document provides an overview of cloud computing and an introduction to Google Cloud. It discusses the different types of cloud services including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It then introduces various Google Cloud Platform (GCP) and G Suite products and services that fall under each category. Examples of code snippets using GCP and G Suite APIs in Python are also provided to demonstrate interacting with these cloud services programmatically.
Exploring Google (Cloud) APIs with Python & JavaScriptwesley chun
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python or JavaScript.
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Exploring Google (Cloud) APIs & Cloud Computing overviewwesley chun
This is a 100-minute tech talk designed for developers to give a comprehensive overview of using Google APIs, primarily those from Google Cloud (G Suite and Google Cloud Platform)
How Google Cloud Platform can help in the classroom/labwesley chun
This is a 90-min tech talk along with hands-on exercises gives a comprehensive, vendor-agnostic overview of cloud computing, primarily targeting educators in the higher education market but is open to any developer. This is followed by an introduction to products in Google Cloud Platform, focusing on its serverless and machine learning products. .
This 2-3 minute presentation is meant to give univeresity hackathoners a brief, high-level overview of Google Cloud and its developer APIs with the purpose of inspiring students to consider these products for their hacks. A longer, more descriptive tech talk comes later.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring 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.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML 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.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
This is an inspirational lightning talk on how developers can take on the future with Google Cloud and other non-Cloud Google tools. It presents various application ideas that are meant to both inspire what's possible as well as show what some of those tools could be.
30-45-min tech talk given at user groups or technical conferences to introducing developers to integrating with Google APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
[Giovanni Galloro] How to use machine learning on Google Cloud PlatformMeetupDataScienceRoma
This document provides an overview of machine learning capabilities on Google Cloud Platform. It discusses how machine learning is used across Google products to improve search ranking and more. It then summarizes the main machine learning capabilities available on GCP, including calling pre-trained models through APIs, building and training custom models on Cloud ML Engine, and using AutoML to build models with little machine learning expertise. The document also briefly introduces upcoming capabilities like Kubeflow for portable machine learning pipelines and AI Hub for discovering and sharing pre-built machine learning solutions.
The document discusses Google Cloud Platform machine learning capabilities for unstructured data like text, speech and images. It introduces the Cloud Vision, Speech and Translate APIs which provide pre-trained machine learning models through REST interfaces to understand unstructured data without requiring ML expertise. Examples are given of using the APIs for tasks like content moderation, sentiment analysis and extracting text/metadata from images.
This document discusses principles for applying continuous delivery practices to machine learning models. It begins with background on the speaker and their company Indix, which builds location and product-aware software using machine learning. The document then outlines four principles for continuous delivery of machine learning: 1) Automating training, evaluation, and prediction pipelines using tools like Go-CD; 2) Using source code and artifact repositories to improve reproducibility; 3) Deploying models as containers for microservices; and 4) Performing A/B testing using request shadowing rather than multi-armed bandits. Examples and diagrams are provided for each principle.
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Power your apps with Gmail, Google Drive, Calendar, Sheets, Slides & morewesley chun
This is a ~90-minute technical introduction to G Suite/Google Apps developer tools, platforms, and APIs. Code samples are in Python+JS. Motivation: encourage developers to write apps integrating with G Suite APIs so they can monetize, taking advantage of the many schools & enterprises that are G Suite users. Delivered sessions at ISTE (Jun 2019), Google Cloud NEXT (Jul 2018), Google Cloud Summit - São Paulo (Nov 2017), DevFest DC (May 2017), DevFest NYC (Nov 2016), and GDG LA DevFest (Dec 2016).
Helixa uses serverless machine learning architectures to power an audience intelligence platform. It ingests large datasets and uses machine learning models to provide insights. Helixa's machine learning system is built on AWS serverless services like Lambda, Glue, Athena and S3. It features a data lake for storage, a feature store for preprocessed data, and uses techniques like map-reduce to parallelize tasks. Helixa aims to build scalable and cost-effective machine learning pipelines without having to manage servers.
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 B)wesley chun
This is one of two presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's serverless platforms and machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
This talk is 1-hr in length.
The other version of this talk ("A") is an 45-mins long and focuses more on APIs platforms.
Serverless computing with Google Cloud (2023-24)wesley chun
This is a half-hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
- The speaker discusses serverless computing platforms on Google Cloud like Cloud Functions and Cloud Run. These platforms allow developers to focus on writing code without worrying about managing servers.
- Serverless computing is growing rapidly due to its ability to auto-scale applications and only charge for compute resources when code is running. This "pay-per-use" model avoids costs from idle servers.
- Popular serverless platforms on Google Cloud include Cloud Functions for running code in response to events, and Cloud Run for deploying containerized applications that are triggered by HTTP requests.
This is a one hour technical talk by @wescpy on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies. There is a bonus section covering serverless in-practice featuring how to think about app development, common use cases, flexibility, best practices, and local dev & testing.
This is a one hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
Designing flexible apps deployable to App Engine, Cloud Functions, or Cloud Runwesley chun
Many people ask, "Which one is better for me: App Engine, Cloud Functions, or Cloud Run?" To help you learn more about them, understand their differences, appropriate use cases, etc., why not deploy the same app to all 3? With this "test drive," you only need to make minor config changes between platforms. You'll also learn one of Google Cloud's AI/ML "building block" APIs as a bonus as the sample app is a simple "mini" Google Translate "MVP". This is a 45- 60-minute talk that reviews the Google Cloud serverless compute platforms then walks through the same app and its deployments. The code is maintained at https://github.com/googlecodelabs/cloud-nebulous-serverless-python
This is a half-hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
Run your code serverlessly on Google's open cloudwesley chun
This is a half-hour technical seminar on Google support of the open source ecosystem, a quick high-level overview/review of cloud computing in general, and then focuses on serverless compute products in Google Cloud and how the platforms are more open than ever!
This is a half-hour technical talk on serverless computing with Python featuring products from the Google Cloud Platform. It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, then shows inspirational examples of apps built using these technologies.
Introduction to Cloud Computing with Google Cloudwesley chun
This is a 20-30 minute technical talk introducing developers to cloud computing including an overview of Google Cloud computing products. There is a special focus on serverless tools as a convenient way for developers to run code. The talk ends with several inspirational apps showcasing what is possible with Google Cloud tools meant to plant a seed as to consider what is possible.
Hackathon opening ceremony 2-5 minute lightning talk introducing Google Cloud tools that students can use for their hacks, whetting their appetites for a more detailed longer tech talk later.
Powerful Google Cloud tools for your hack (2020)wesley chun
You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? This session takes hackathon participants on a deeper dive from the opening ceremony lightning intro. In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring students for their hacks. We'll look closely at our serverless platforms & machine learning APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started so you'll know what to do with those Cloud credits you got from MLH!
Google Apps Script: Accessing G Suite & other Google services with JavaScriptwesley chun
This document provides an overview of Google Apps Script, including its capabilities, use cases, and coding examples. Some key points:
- Google Apps Script is a JavaScript runtime that allows automation of G Suite applications and integration with other Google and external services.
- It can be used to extend functionality within G Suite editors like Sheets, Docs and Slides through add-ons, or to build standalone web apps and microservices.
- Examples demonstrate how to access APIs to integrate with services like Google Maps, Gmail, Calendar and Natural Language, as well as build bots for Hangouts Chat.
- The document also shows how Apps Script can be used to "glue" together Google Cloud Platform
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
1. Google developer tools (mainly GCP) & an
Easyier path to machine learning
Mountain View :: Winter 2024
Wesley Chun
Principal, CyberWeb
@wescpy@
Principal Consultant, CyberWeb
● Mission: produce accelerated Python
developers, enable developers to be
successful using Google Cloud and
other Google developer tools & APIs
● Focus: Python, Google Cloud (GCP) &
Google Workspace (GWS) APIs; GAE
migrations; Google X-product sol'ns
● Services: technical consulting,
training, engineering, speaking, code
samples, hands-on tutorials, public
technical content (blogs, social, etc.)
About the speaker
Previous experience / background
● Software Engineer & Developer Advocate
○ Google, Sun, HP, Cisco, EMC, Xilinx &
○ Original Yahoo!Mail engineer/SWE
● Technical trainer, teacher, instructor
○ Teaching Math, Linux, Python since '83
○ Adjunct CS Faculty at local SV colleges
● Python community member
○ Popular Core Python series author
○ Python Software Foundation Fellow
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)
GWS Dev Show
goo.gl/JpBQ40
GAE migration
bit.ly/3xk2Swi
2. AI & ML session: why & agenda
● Big data is everywhere, giving rise to increasingly challenging problems
● AI/ML analyzes data, gives novel insight, and produces new content
● Requiring certain level of math/statistics gives AI/ML learning curve
● APIs backed by ML provides an easier path: if you can call APIs. you can...
● Leverage the power of ML, gain experience, and accelerate learning
What is ML?
1
Introducing
Google Cloud
2
Google APIs
primer
3
Cloud ML APIs
4 5
Other Google
devtools & APIs
7
Wrap-up
6
Inspiration
01
What is machine learning?
Can we make computers "smarter?"
3. AI
Solve problems by
"mimicking" human
intelligence
(logic/rules-based)
ML
Learn from observed
patterns in massive
data sets & formulate
informed decisions
from those insights
DL
More sophisticated ML
models learning with no
human intervention; use
neural networks to
tackle more complex
problems
4. AI & machine learning
Puppy or muffin?
Source: twistedsifter.com/2016/03/
puppy-or-bagel-meme-gallery
Machine learning is learning
from rules plus experience.
6. Three different ways to train ML models
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
7. Deep Learning model types
Discriminative/Predictive AI
● Used to classify or predict
● Typically trained on labeled dataset
● Learns relationship between data point features and labels
Generative AI (single-use or multimodal)
● Generates new data similar to data a model was trained on
● Understands distribution of data & how likely a given example is
● Can "predict" the next or similar "item" in dataset
Global view
Problem
● 1B ppl depend on seafood
● 85% at/over-fishing or recovering
● 20% caught illegal, undoc'd, unreg'd
● Analysts monitoring unscalable
One solution
● globalfishingwatch.org/map
● Machine-learning classifiers:
○ Ship type: cargo, tug, sail, fishing
○ Ship size
○ Gear: longline, purse seine, trawl
○ Movement tracking: when and
where vessels are fishing
9. Organize data
Use machines to
flesh out the
model from data
Collect
data
Create model
Deploy fleshed
out model
In reality what ML is
Large Datasets Good Models Lots Of Computation
Keys to Successful Machine Learning
11. Vertex AI task-specific models: ML "building block" APIs
● Gain insights from data using GCP's
pre-trained machine learning models
● Leverage the same technology as Google
Translate, Photos, and Assistant
● Requires ZERO prior knowledge of ML
● If you can call an API, you can use AI/ML!
● cloud.google.com/products/ai/building-blocks
Vision Video
Intelligence
Speech
(S2T & T2S)
Natural
Language
Translation
Full Spectrum of AI & ML Offerings
App developer Data scientist,
developer
Data scientist, Researcher
(w/infrastructure access &
DevOps/SysAdmin skills)
Vertex AI
platform
Build custom models,
use OSS SDK on fully-
managed infrastructure
Single-task
model APIs
App developer,
data scientist
Use or fine-tune
pre-built models
Use pre-built/pre-
trained models
Build custom models, use/
extend OSS SDK, self-manage
training infrastructure
LMM/large
multimodal
model API
Auto ML
13. General steps
1. Go to Cloud Console
2. Login to Google/Gmail account
(Workspace domain may require admin approval)
3. Create project (per application)
4. Enable APIs to use
5. Enable billing (CC, Free Trial, etc.)
6. Download client library(ies)
7. Create & download credentials
8. Write code*
9. Run code (may need to authorize)
Google APIs: how to use
*In your code
1. Import API client library
2. Create API client object
3. Use client to make API Calls
Costs & pricing
● GCP & GMP: pay-per-use (CC req'd)
● GWS: "subscription" (incl. $0USD/mo.)
● GMP: $200/mo. free usage
● GCP Free Trial: $300/1Q
● GCP "Always Free" tier
○ Some products have free tier
○ Daily or monthly quota
○ Must exceed to incur billing
● More on both programs at
cloud.google.com/free
14. Cloud/GCP console
console.cloud.google.com
● Hub of all developer activity
● Applications == projects
○ New project for new apps
○ Projects have a billing acct
● Manage billing accounts
○ Financial instrument required
○ Personal or corporate credit cards,
Free Trial, and education grants
● Access GCP product settings
● Manage users & security
● Manage APIs in devconsole
● View application statistics
● En-/disable Google APIs
● Obtain application credentials
Using Google APIs
goo.gl/RbyTFD
API manager aka Developers Console (devconsole)
console.developers.google.com
15. Client libraries and credentials types
● Two different client library types
○ Platform-level client libraries (lower-level)
■ Multiple product groups as a "lowest-common denominator"
■ Install: developers.google.com/api-client-library
○ Product-level client libraries (higher-level)
■ Custom client libraries made specifically for 1 product or product group
■ Found on product or product group page(s)
● Three different credentials types
○ Simple: API keys (access public data)
■ Simplest form of authorization: an API key; tied to a project
○ Authorized: OAuth client IDs (access data owned by [human] user)
■ Provides additional layer of security via OAuth2 (RFC 6749)
○ Authorized: service accounts (access data owned by an app/robot user)
■ Provides additional layer of security via OAuth2 or JWT (RFC 7519)
Blog series:
dev.to/wescpy
&
Google APIs client
libraries for many
languages; demos in
developers.google.com/api-
client-library
cloud.google.com/apis/docs
/cloud-client-libraries
16. OAuth2 or
API key
HTTP-based REST APIs 1
HTTP
2
Google APIs request-response workflow
● Application makes request
● Request received by service
● Process data, return response
● Results sent to application
(typical client-server model)
04
Cloud ML APIs
Easier path to ML by calling APIs!
17. Machine Learning: Cloud Vision
Google Cloud Vision API lets developers
extract metadata and understand the
content of an image, identify & detect
objects/labels, text/OCR, landmarks,
logos, facial features, products, XC, etc.
cloud.google.com/vision
from google.cloud import vision
image_uri = 'gs://cloud-samples-data/vision/using_curl/shanghai.jpeg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.label_detection(image=image)
print('Labels (and confidence score):')
print('=' * 30)
for label in response.label_annotations:
print(label.description, '(%.2f%%)' % (label.score*100.))
Vision: label annotation/object detection
18. $ python3 label-detect.py
Labels (and confidence score):
==============================
People (95.05%)
Street (89.12%)
Mode of transport (89.09%)
Transport (85.13%)
Vehicle (84.69%)
Snapshot (84.11%)
Urban area (80.29%)
Infrastructure (73.14%)
Road (72.74%)
Pedestrian (68.90%)
Vision: label annotation/object detection
g.co/codelabs/vision-python
Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
reveals the structure and meaning
of text, performing sentiment
analysis, content classification,
entity extraction, and syntactical
structure analysis; multi-lingual
cloud.google.com/language
19. Simple sentiment & classification analysis
from google.cloud import language
TEXT = '''Google, headquartered in Mountain View, unveiled the new
Android phone at the Consumer Electronics Show. Sundar Pichai said
in his keynote that users love their new Android phones.'''
NL = language.LanguageServiceClient()
document = language.types.Document(content=TEXT,
type=language.enums.Document.Type.PLAIN_TEXT)
print('TEXT:', TEXT) # sentiment analysis
sent = NL.analyze_sentiment(document).document_sentiment
print('nSENTIMENT: score (%.2f), magnitude (%.2f)' % (sent.score, sent.magnitude))
print('nCATEGORIES:') # content classification
categories = NL.classify_text(document).categories
for cat in categories:
print('* %s (%.2f)' % (cat.name[1:], cat.confidence))
Simple sentiment & classification analysis
$ python nl_sent_simple.py
TEXT: Google, headquartered in Mountain View, unveiled the new Android
phone at the Consumer Electronics Show. Sundar Pichai said in
his keynote that users love their new Android phones.
SENTIMENT: score (0.20), magnitude (0.50)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)
20. Machine Learning: Cloud Video Intelligence
Google Cloud Video Intelligence
API makes videos searchable, and
discoverable, by extracting
metadata. Other features: object
tracking, shot change detection,
and text detection
cloud.google.com/video-intelligence
Machine Learning: Cloud Speech
Google Cloud Speech APIs enable
developers to convert
speech-to-text and vice versa
cloud.google.com/speech
cloud.google.com/text-to-speech
21. Machine Learning: Cloud Translation
Access Google Translate
programmatically through this
API; translate an arbitrary
string into any supported
language using state-of-the-art
Neural Machine Translation
cloud.google.com/translate
Translating text "Hello World" (JS/Node.js)
const {TranslateClient} = require('@google-cloud/translate');
const TRANSLATE = new TranslateClient();
const text = 'Hello World!'; // Text to translate
const target = 'gu'; // Target language
// Translate text to Gujarti
const translation = await TRANSLATE.translate(text,
{from: 'en', to: target}));
// "Translation: હેલો વ ડર્લ્ડ"
console.log('Translation: ', translation[0]);
22. Machine Learning: AutoML
AutoML: suite of cloud APIs for
developers with limited machine
learning expertise; take task-specific
pre-trained model, perform additional
training with your data to finetune
that model
(Translation, Vision, Natural Language,
Video Intelligence, Tables)
cloud.google.com/automl
cloud.google.com/automl-tables
● What is it, and how does it work?
○ These APIs backed by pre-trained models
○ Likely less suitable for your data
○ Finetune (further customize/train) these models with your data
○ Without sophisticated ML background
○ Translate, Vision, Natural Language, Video Intelligence, Tables
○ cloud.google.com/automl
● Steps
a. Prep your training data
b. Create dataset
c. Import items into dataset
d. Train/"finetune" model
e. Evaluate/validate model
f. Make predictions
Cloud AutoML
23. Machine Learning: Vertex AI
Cloud Vertex AI (formerly AI Platform) is a
managed service providing: 1) APIs backed by
pre-trained models, 2) ability to further
train those models, 3) Jupyter Notebook
support, 4) infrastructure to build, train &
deploy ML models (PyTorch, scikit-learn,
TensorFlow) & serve models, all in 1 platform
cloud.google.com/vertex-ai
Machine Learning: Cloud Generative AI
Google Cloud Generative AI is a set of
tools & APIs (like Vertex AI & Duet
AI) that make it easier for developers
to build generative AI-powered
services & applications; includes the
Gemini foundation model as well as
open source & 3rd-party models
cloud.google.com/ai/generative-ai
24. import vertexai
import vertexai.preview.generative_models as genai
PROMPT = 'What is the meaning of life?'
MODEL = 'gemini-pro'
print('** GenAI text: %r model & prompt %rn' % (
MODEL, PROMPT))
vertexai.init()
model = genai.GenerativeModel(MODEL)
response = model.generate_content(PROMPT)
print(response.text)
Cloud Vertex AI: Gemini API
cloud.google.com/vertex-ai/docs/generative-
ai/start/quickstarts/quickstart-multimodal
import google.generativeai as genai
from settings import API_KEY
PROMPT = 'What is the meaning of life?'
MODEL = 'gemini-pro'
print('** GenAI text: %r model & prompt %rn' % (
MODEL, PROMPT))
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel(MODEL)
response = model.generate_content(PROMPT)
print(response.text)
Google AI: Gemini API
ai.google.dev/tutorials
25. 05
Other GCP & Google
APIs & developer tools
These may also be helpful
Other Cloud APIs/services
"Friends of AI/ML" companion services
26. Storing and Analyzing Data: BigQuery
Google BigQuery: fully-managed data
warehouse for large-scale data
analytics with built-in machine
learning (BQML); issue SQL queries
across multi-terabytes of data. BQ
Sandbox lets anyone query up to
1TB/mo for free with no obligation
cloud.google.com/bigquery
BigQuery: querying Shakespeare words
TITLE = "The most common words in all of Shakespeare's works"
QUERY = '''
SELECT LOWER(word) AS word, sum(word_count) AS count
FROM [bigquery-public-data:samples.shakespeare]
GROUP BY word ORDER BY count DESC LIMIT 10
'''
rsp = BQ.jobs().query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
print('t'.join(col['name'].upper() # HEADERS
for col in rsp['schema']['fields']))
print('n'.join('t'.join(str(col['v']) # DATA
for col in row['f']) for row in rsp['rows']))
27. Top 10 most common Shakespeare words
$ python bq_shake.py
*** Results for "The most common words in all of Shakespeare's works":
WORD COUNT
the 29801
and 27529
i 21029
to 20957
of 18514
a 15370
you 14010
my 12936
in 11722
that 11519
● BigQuery public data sets: cloud.google.com/bigquery/public-data
● BQ sandbox (1TB/mo free): cloud.google.com/bigquery/docs/sandbox (see blog post)
● Other public data sets: cloud.google.com/public-datasets (Google Cloud),
research.google/tools/datasets (Google Research), and Kaggle (kaggle.com)
● COVID-19 BigQuery data sets
○ How to use our data sets (see blog post)
○ JHU Coronavirus COVID-19 Global Cases data set
○ List of all COVID-19 data sets
● Cloud Life Sciences API: cloud.google.com/life-sciences (see blog post)
● Cloud Healthcare API: cloud.google.com/healthcare (see blog post)
BigQuery and public data sets
28. Storing Data: Cloud Storage, Filestore, Persistent Disk
cloud.google.com/storage
cloud.google.com/filestore
cloud.google.com/persistent-disk
Storing Data: Cloud SQL & AlloyDB
Relational DB servers in the cloud;
High-performance, fully-managed
600MB to 416GB RAM; up to 64 vCPUs
Up to 10 TB storage; 40,000 IOPS
Types:
MySQL
Postgres; AlloyDB for high perf
SQLServer
cloud.google.com/databases
cloud.google.com/{sql, alloydb}
29. Storing Data: Cloud Firestore
The best of both worlds: the next
generation of Cloud Datastore
(w/product rebrand) plus features
from the Firebase realtime database
(For choosing between Firebase & Cloud Firestore: see
firebase.google.com/docs/firestore/rtdb-vs-firestore;
for choosing between Firestore Datastore & Firestore Native modes:
see cloud.google.com/datastore/docs/firestore-or-datastore)
cloud.google.com/firestore
Google Workspace: Google Sheets
Sheets API gives you programmatic
access to spreadsheets; perform
(w/code) almost any action you can
do from the web interface as a user
developers.google.com/sheets
30. Try our Node.js customized reporting tool codelab:
g.co/codelabs/sheets
Why use the Sheets API?
data visualization
customized reports
Sheets as a data source
Migrate SQL data to a Sheet
# read SQL data then create new spreadsheet & add rows into it
FIELDS = ('ID', 'Customer Name', 'Product Code',
'Units Ordered', 'Unit Price', 'Status')
cxn = sqlite3.connect('db.sqlite')
cur = cxn.cursor()
rows = cur.execute('SELECT * FROM orders').fetchall()
cxn.close()
rows.insert(0, FIELDS)
DATA = {'properties': {'title': 'Customer orders'}}
SHEET_ID = SHEETS.spreadsheets().create(body=DATA,
fields='spreadsheetId').execute().get('spreadsheetId')
SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID, range='A1',
body={'values': rows}, valueInputOption='RAW').execute()
Migrate SQL data
to Sheets
goo.gl/N1RPwC
31. Google and Jupyter Notebooks
Users have many ways to access an indispensable data science tool
● Google Cloud Vertex AI Workbench
○ cloud.google.com/notebooks
● Kaggle
○ kaggle.com
● Google Research CoLaboratory
○ colab.research.google.com
● Google Cloud Dataproc Hub
○ cloud.google.com/dataproc/docs/tutorials/dataproc-hub-overview
● Google Cloud Datalab
○ cloud.google.com/datalab/docs/how-to/working-with-notebooks
Where to run your code (without VMs)
GCP/GWS serverless compute platforms
32. cloud.google.com/hosting-options#hosting-options
Google Cloud compute option spectrum
Compute
Engine
Kubernetes
Engine (GKE)
Cloud Run
on Anthos
Cloud Run
(fully-mgd)
App Engine
(Flexible)
App Engine
(Standard)
Cloud
Functions
> Google Compute Engine configurable
VMs of all shapes & sizes, from
"micro" to 416 vCPUs, 11.776 TB
RAM, 256 TB HDD/SSD plus Google
Cloud Storage for data lake "blobs"
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux,
Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016, 1803,
1809, 1903/2019, 1909)
cloud.google.com/compute
cloud.google.com/storage
Yeah, there are VMs & big disk… but why*?
33. Serverless: what & why
● What is serverless?
○ Misnomer (a "PMM") :-)
○ "No worries"
○ Developers focus on writing code & solving business problems*
○ Servers (physical & virtual) completely abstracted away from the user*
● Why serverless?
○ Fastest growing segment of cloud... per analyst research:
■ $1.9B (2016) and $4.25B (2018) ⇒ $7.7B (2021), $14.93B (2023), and $21.1B (2025)^
■ $4.18B (2018) and $6.05B (2020) ⇒ $31.53B (2026) and $53.08B (2028)†
○ What if you go viral? Autoscaling: your new best friend
○ What if you don't? Code not running? You're not paying.
* Forbes (May 2018)
^ (in USD) CB Insights (Sep 2018), MarketsandMarkets™ (Jan 2019)
† (in USD) Reports and Data (Jul 2019 , Jan 2020, and Oct 2021)
Running Code: App Engine
Got a great app idea? Now what?
VMs? Operating systems? Big disk?
Web servers? Load balancing?
Database servers? Autoscaling?
With App Engine, you don't think
about those. Just upload your
code; GCP takes care of the rest.
>
cloud.google.com/appengine
34. Why does App Engine exist?
● Focus on code not DevOps
○ Web app or mobile backend
● Enhance productivity
● Deploy globally
● Fully-managed
● Auto-scaling
● Pay-per-use
● Familiar languages
● Test w/local dev server
● "Bundled" services like DB,
caching, tasks, storage, etc.
Hello World (Python "MVP")
app.yaml
runtime: python39
main.py
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello World!'
requirements.txt
flask
Deploy:
$ gcloud app deploy
Access globally:
PROJECT_ID.appspot.com
cloud.google.com/appengine/docs/standard/python3/quickstart
35. Running Code: Cloud Functions
Don't have an entire app? Just want
to deploy small microservices or
"RPCs" online globally? That's what
Google Cloud Functions are for!
(+Firebase version for mobile apps)
cloud.google.com/functions
firebase.google.com/products/functions
Why does Cloud Functions exist?
● Don't have entire app?
○ No framework "overhead" (LAMP, MEAN...)
○ Deploy short utilities (alerts, ETL...), monoliths →
loosely-coupled microservices, event-driven tasks
● Event-driven
○ Triggered via HTTP or background events
■ Pub/Sub, Cloud Storage, Firebase, etc.
○ Auto-scaling & highly-available; pay per use
● Flexible development environment
○ Cmd-line or developer console (in-browser)
○ Develop/test locally with Functions Framework
● Cloud Functions for Firebase
○ Mobile app use-cases
36. main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python39 --trigger-http
Access globally (curl):
$ curl REGION-PROJECT_ID.cloudfunctions.net/hello
Access globally (browser):
https://REGION-PROJECT_ID.cloudfunctions.net/hello
Hello World (Python "MVP")
cloud.google.com/functions/docs/quickstart-python
Running Code: Cloud Run
Got a containerized app? Want its
flexibility along with the convenience
of serverless that's fully-managed
plus auto-scales? Google Cloud Run is
exactly what you're looking for!
Need custom HW? Cloud Run on GKE
cloud.google.com/run
37. The rise of containers... ● Any language
● Any library
● Any binary
● Ecosystem of base images
● Industry standard
FLEXIBILITY
“We can’t be locked in.”
“How can we use
existing binaries?”
“Why do I have to choose between
containers and serverless?”
“Can you support language _______ ?”
Serverless inaccessible for some...
CONVENIENCE
38. Cloud Run: code, build, deploy .js .rb .go
.sh
.py ...
● Any language, library, binary
○ HTTP port, stateless
● Bundle into container
○ Build with Docker OR Cloud Build
○ Image ⇒ Container/Artifact Registry
● Deploy to Cloud Run (managed or GKE)
● GitOps: CD push-to-deploy from Git
○ See documentation & announcement
○ CI/CD with GitHub Actions tutorial
● Cloud Buildpacks: Docker, Dockerfiles,
and knowledge of containers optional
State
HTTP
Hello World (Python "MVP")
main.py
import os
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
cloud.google.com/run/docs/quickstarts/build-and-deploy
requirements.txt
flask
39. Hello World (Python "MVP")
Dockerfile
FROM python:3-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]
.dockerignore
Dockerfile
README.md
*.pyc
*.pyo
.git/
__pycache__
Build (think docker build and docker push) then deploy (think docker run):
$ gcloud builds submit --tag gcr.io/PROJ_ID/IMG_NAME
$ gcloud run deploy SVC_NAME --image gcr.io/PROJ_ID/IMG_NAME
OR… Build and Deploy (1-line combination of above commands):
$ gcloud run deploy SVC_NAME --source .
Access globally:
SVC_NAME-HASH-REG_ABBR.a.run.app
Docker &
Dockerfile
OPTIONAL!!
Flexibility in options
Cloud
Functions
App
Engine
Cloud
Run
local
server
● "Nebulous" sample web app
○ Flask/Python 2 or 3
○ Express/Node.js 10+
○ Uses Cloud Translation API
● Deployable to on-prem server
● Also GCP serverless compute
○ App Engine
○ Cloud Functions
○ Cloud Run
● With only config changes
● No changes to app code
Cloud
Translation
My "Google Translate" MVP
goo.gle/2Y0ph5q
youtu.be/eTotLOVR7MQ
40. 06
Inspiration
Use multiple Google APIs
to create unique solutions
Cloud image processing workflow
Archive and analyze GWS data (images) with GCP
44. Cloud image processing workflow
def drive_get_file(fname):
rsp = DRIVE.files().list(q="name='%s'" % fname).execute().get['files'][0]
fileId, fname, mtype = rsp['id'], rsp['name'], rsp['mimeType']
blob = DRIVE.files().get_media(fileId).execute()
return fname, mtype, rsp['modifiedTime'], blob
def gcs_blob_upload(fname, bucket, blob, mimetype):
body = {'name': fname, 'uploadType': 'multipart',
'contentType': mimetype}
return GCS.objects().insert(bucket, body, blob).execute()
def vision_label_img(img, top):
body = {'requests': [{'image': {'content': img}, 'features':
[{'type': 'LABEL_DETECTION', 'maxResults': top}]}]}
rsp = VISION.images().annotate(
body=body).execute().get('responses', [{}])[0]
return ', '.join('%s (%.2f%%)' % (label['description'],
label['score']*100.) for label in rsp['labelAnnotations'])
def sheet_append_row(sheet, row):
rsp = SHEETS.spreadsheets().values().append(
spreadsheetId=sheet, range='Sheet1',
body={'values': rows}).execute()
return rsp.get('updates').get('updatedCells')
def main(fname, bucket, sheet_id, top):
fname, mtype, ftime, data = drive_get_img(fname)
gcs_blob_upload(fname, bucket, data, mtype)
rsp = vision_label_img(data, top)
sheet_append_row(sheet_id, [fname, mtype,
ftime, len(data), rsp])
API method calls in Bold
Driver calls in Bold Italics
● Project goal: Imagining an actual enterprise use case and solve it!
● Specific goals: free-up highly-utilized resource, archive data to
colder/cheaper storage, analyze images, generate report for mgmt
● Download image binary from Google Drive
● Upload object to Cloud Storage bucket
● Send payload for analysis by Cloud Vision
● Write back-up location & analysis results into Google Sheets
● Blog post: goo.gle/3nPxmlc (original post); Cloud X-post
● Codelab: free, online, self-paced, hands-on tutorial
● g.co/codelabs/drive-gcs-vision-sheets
● Application source code
● github.com/wescpy/analyze_gsimg
App summary
45. Hangouts Chat Productivity Tracker
Chat bot that's GCP & Google Workspace (formerly G Suite) aware
Productivity tracker Hangouts Chat bot
Google Workspace
(formerly G Suite)
GCP
Sheets Natural Language
START
or LOG
END
Hangouts
Chat
App
Engine
Cloud
SQL
46. App summary
● Motivation
● Do coding contract jobs during school year
● Needed to track time spent on work (and non-work)
● Who doesn't want to be more productive?
● Hangouts Chat bot framework & API... build bots to:
● Automate workflows
● Query for information
● Other heavy-lifting
● Google Workspace (formerly G Suite) app that leverages app-hosting, NL processing, and storage
tools from GCP
● Application source code
● github.com/gsuitedevs/hangouts-chat-samples/tree/master/python/productivity_tracker
● Presented at GDG Silicon Valley (Aug 2018)
● meetup.com/gdg-silicon-valley/events/252858660
47. 07
Wrap-up
Summary & resources
Summary: AI & ML session
● What is machine learning again?
○ Solving harder problems by making computers smarter
○ "Using data to answer questions.” ~Yufeng Guo, Google Cloud
● How do you do machine learning again?
○ Collect lots of data
○ Build and train your model then validate it
○ Use your model to make predictions on new data
● Do you need lots of machine learning experience to get started?
○ No: use pre-trained models available via APIs
○ No: need to do training? Consider using AutoML APIs
○ Build your experience then use standard OSS library when ready
48. ● Documentation (most APIs have "Quickstarts")
○ GCP: cloud.google.com/{docs,appengine,functions,run,vision,automl,translate,language,
speech,texttospeech,video-intelligence,firestore,bigquery,compute,storage,gpu,tpu}
○ GWS: developers.google.com/{gsuite,drive,calendar,gmail,docs,sheets,slides,apps-script}
● Introductory "codelabs" ([free] self-paced, hands-on tutorials)
○ GWS APIs: g.co/codelabs/gsuite-apis-intro (featuring Drive API)
○ Apps Script: g.co/codelabs/apps-script-intro
○ App Engine: codelabs.developers.google.com/codelabs/cloud-app-engine-python
○ Cloud Functions: codelabs.developers.google.com/codelabs/cloud-starting-cloudfunctions
○ Cloud Run: codelabs.developers.google.com/codelabs/cloud-run-hello-python3
○ Others: g.co/codelabs (all Google codelabs) and g.co/codelabs/cloud (GCP-only)
● Videos: youtube.com/GoogleCloudPlatform (GCP) and goo.gl/JpBQ40 (GWS)
● Code samples: github.com/GoogleCloudPlatform (GCP) and github.com/googleworkspace (GWS)
● Cloud Free Trial (new users) and Always Free (daily/monthly tier) programs: cloud.google.com/free
● Know AWS/Azure? Compare with GCP products at cloud.google.com/docs/compare/aws
● Language support: cloud.google.com/{python,java,nodejs,go,php,ruby,dotnet}
Resources (industry)
quickdraw.withgoogle.com
Cloud Vision demo: Quick Draw game
experiments.withgoogle.com/quick-draw
49. FYI and FYA (if you/your students love comics)
cloud.google.com/products/ai/ml-comic-[12]
... ...
Other Google APIs & platforms
● Google Workspace (G Suite) (code Gmail, Drive, Docs, Sheets, Slides!)
○ developers.google.com/gsuite
● Firebase (mobile development platform and RT DB plus ML-Kit)
○ firebase.google.com and firebase.google.com/docs/ml-kit
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
○ goo.gle/datastudio-course
● Actions on Google/Assistant/DialogFlow (voice apps)
○ developers.google.com/actions
● YouTube (Data, Analytics, and Livestreaming APIs)
○ developers.google.com/youtube
● Google Maps (Maps, Routes, and Places APIs)
○ developers.google.com/maps
● Flutter (native apps [Android, iOS, web] w/1 code base[!])
○ flutter.dev
50. Bring me to your organization
... it is my job to help you!
● "Transfer of Info" tech talks
● Half- or full-day seminars
● Hands-on "codelab" workshops
● Multi-day training courses
● Engineering consulting
● Migration strategy & planning
● cyberwebconsulting.com
● appenginemigration.com
Slides
you're looking
at them now
Work
cyberwebconsulting.com
Books
corepython.com
Blog
dev.to/wescpy
Img proc wkflw
goo.gle/3nPxmlc
AI/ML codelabs
codelabs.developers.google.com/?
category=aiandmachinelearning
Nebulous serverless
goo.gle/2Y0ph5q
youtu.be/eTotLOVR7MQ
Progress bars
goo.gl/69EJVw
Thank you! Questions?
Wesley Chun
Principal Consultant, CyberWeb
Python, GCP & GWS specialist
@wescpy (Tw/X, SO, GH, IG, LI)