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07:27

Advanced features on TensorFlow Serving

Wei Wei, Developer Advocate at Google, shares several advanced TensorFlow Serving features (experimental). Learn how TF Serving can tend JAX models, serve non-TensorFlow models with new servables, and remotely predict RPC with distributed serving. Resources: TF Serving model server parameters → https://goo.gle/3tQ99Qq Intro to JAX: Accelerating Machine Learning research → https://goo.gle/3xOKBsq Convert JAX models to […]
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10:31

3D MRI brain segmentation – Made with TensorFlow.js

With a background in image processing and Machine Learning, Mohamed Masoud created ”Brain Chop”, a web-based end-to-end solution that can perform 3D MRI brain segmentation using TensorFlow.js. Learn how this tool is not only changing the world of medicine but how people interact with medical technology with its simple, user-friendly interface. Try it for yourself: […]
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05:51

TensorFlow Serving client examples

Wei Wei, Developer Advocate at Google, walks through how to send REST and gRPC prediction requests to TensorFlow serving backend with Python and C++. Don’t worry if your client uses another language, below there are new sets of codelabs covering web, Android, Flutter, and iOS frontends. Codelabs Image Classification with TensorFlow Serving (Web) → https://goo.gle/3tQfqMb […]
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06:34

Deploying production ML models with TensorFlow Serving overview

Wei Wei, Developer Advocate at Google, overviews deploying ML models into production with TensorFlow Serving, a framework that makes it easy to serve the production ML models with low latency and high throughput. Learn how to start a TF Serving model server and send POST requests using the command line tool. Wei covers what it […]
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11:58

The “spell check” of design systems – Made with TensorFlow.js

Meet Joo Hyung Park, a software engineer and product designer, from South Korea who is working on the “spell check” of design systems. His project, Figma ML, uses TensorFlow.js to go through four checkpoints to recognize key elements of UI design using a custom object detection API to determine if there are any design issues […]
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01:52:21

TensorFlow.js Community "Show & Tell" #7

6 new demos from the #MadeWithTFJS global community pushing the boundaries of what’s possible for Web MLv/ on device machine learning using JavaScript. Give your next web app superpowers in the browser and beyond. Hosted by Jason Mayes. Want to be on our next show? Use the #MadeWithTFJS tag on social to share your best […]
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08:09

What have AI language models learned?

AI and ML researchers developed Large Language Models (LLMs) like BERT to help machines interact with natural language, improving their abilities in tasks like chat and translation! Nithum Thain, Senior Software Engineer at Google PAIR, highlights the Explorable “What Have Language Models Learned” by Adam Pearce and shares what we can learn about BERT by […]
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05:55

Top AI and ML announcements from Google I/O 2022

Catch the top takeaways in AI and ML from Google I/O 2022. Laurence Moroney shares the latest updates, tools, and guidance available for creating and deploying models with TensorFlow tech. From responsible AI to Coral Dev Board Micro, MLOps, TensorFlow.js, and beyond. There are sessions, workshops, and learning pathways for deeper understanding. Watch more: All […]
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16:57

Build a smart IoT device with TensorFlow Lite and Raspberry Pi

Learn how to build smart IoT devices using TensorFlow Lite and Raspberry Pi. Discover pre-trained object detection and sound classification models that you can use off-the-shelf to “see” and “hear” the surrounding environments. If the off-the-shelf models don’t satisfy your app’s needs, learn how to collect training data and train your own custom models using […]
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26:37

Tips and tricks for distributed large model training

Discover several different distribution strategies and related concepts for data and model parallel training. Walk through an example of training a 39 billion parameter language model on TPUs, and conclude with the challenges and best practices of orchestrating large scale language model training. Resource: TensorFlow website → https://goo.gle/3KejoUZ Speakers: Nikita Namjoshi, Vaibhav Singh Watch more: […]
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15:50

Introducing Coral Dev Board Micro

Hear the latest updates from Google Coral, a platform for developing and deploying AI/ML solutions at the edge. This session reviews the latest Coral ecosystem development and industry innovations by developers around the world. Also, it introduces the new Coral Dev Board Micro, a dual-core & multi-model-capable microcontroller board that supports TF Micro models for […]
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56:54

How to customize Machine Learning models the simple way

Learn the easiest way to customize pretrained Machine Learning models to your own data. Speaker: Gus Martins Watch more: All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions ML/AI at I/O 2022 playlist → https://goo.gle/IO22_ML-AI All Google I/O 2022 workshops → https://goo.gle/IO22_Workshops Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO Source of this TensorFlow AI Video
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13:21

An introduction to MLOps with TensorFlow Extended (TFX)

Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. An ML application in production requires modern software development methodology, as well as issues unique to ML and data science. Hear about the importance of MLOps, the use of ML pipeline architectures for implementing production ML applications, […]
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17:58

TensorFlow.js: From prototype to production, what's new in 2022?

Join the TensorFlow.js team to learn about the latest updates around Web ML. Discover how you can enable Web ML for your business application or creative idea to build the next generation of web apps powered by AI and machine learning. Learn about new models, performance improvements, and improved tooling, all accessible from within the […]
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11:39

Setup Apple Silicon Mac for Machine Learning in 11 minutes (PyTorch edition)

Setup your Apple M1, M1 Pro, M1 Max or M1 Ultra Mac for data science and machine learning with PyTorch. Get the code on GitHub – https://github.com/mrdbourke/pytorch-apple-silicon PyTorch on Mac announcement blog post – https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ Learn PyTorch – https://learnpytorch.io Setup Apple M1 for TensorFlow – https://youtu.be/_1CaUOHhI6U Other links: Learn ML (beginner-friendly courses I teach) – […]
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20:15

AI and Machine Learning for developers

To watch this keynote interpreted in American Sign Language (ASL), please click here → https://goo.gle/38aUqYX Learn what Google is doing in the AI and machine learning space, from developer APIs to state-of-the-art research. Resource: TensorFlow website → https://goo.gle/36VewFG Speakers: Laurence Moroney, Alex Spinelli, Diane Wu Watch more: All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions ML/AI […]
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19:21

A developer's guide to responsible AI review processes

From startups to corporations across industries, organizations are creating AI principles and ethics review processes to complement technical approaches to developing ML and AI responsibly. Listen to emerging socio-technical practices, ML tools, and lessons learned from Google’s ethics review teams who support developers as they build products. Resource: TensorFlow website → https://goo.gle/3KejoUZ Speakers: Madeleine Elish, […]
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25:09

Apply responsible AI principles when building remote sensing datasets

Learn how to apply responsible AI frameworks while making decisions related to datasets and coding with large-scale social benefit in mind. Walk through a version of the developer tutorial, “Hello [Dynamic] World,” and learn how to use a new dataset using Google Earth Engine data responsibly. Presented in collaboration with Google’s Geo for Good team. […]
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22:13

Adding Machine Learning to your developer toolbox

Machine learning can be a challenging field, as there are a lot of new concepts to learn from the perspectives of mobile, web, and back-end developers. Find out how easy it is to start using machine learning in your app with TensorFlow tools. Resource: TensorFlow website → https://goo.gle/3KejoUZ Speaker: Gus Martins Watch more: All Google […]
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34:52

Product fairness testing for Machine Learning developers

Product fairness testing is an essential step for developers as they build ML models responsibly. Discover the key steps you should take to conduct holistic product fairness tests, in addition to understanding both the technical and user-centric societal perspectives you should incorporate into your process. Walk away understanding the end-to-end journey of assessing a dataset […]
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36:36

Transfer learning in JavaScript with TensorFlow.js

Learn how to make your very own version of the popular Teachable Machine website. Use TensorFlow.js live in the web browser to train a custom machine learning model to then recognize any object you show it, made possible through a technique known as transfer learning. Building on that concept, you can quickly create new models […]
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