Inside TensorFlow: Building ML infra

TensorFlow · Beginner ·📐 ML Fundamentals ·5y ago
Skills: ML Pipelines80%
In this episode of Inside TensorFlow, Software Engineer Mingsheng Hong presents building ML infra. Mingsheng will be sharing some research and engineering problems in building machine learning infrastructure. TFRT blog post → https://goo.gle/2KLH3z6 Add the Inside TensorFlow playlist → https://goo.gle/Inside-TensorFlow Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow
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1 The TensorFlow YouTube Channel is Here!
The TensorFlow YouTube Channel is Here!
TensorFlow
 2 Answering Your TF Questions #AskTensorFlow
Answering Your TF Questions #AskTensorFlow
TensorFlow
 3 Chatting With the TensorFlow Community (TensorFlow Meets)
Chatting With the TensorFlow Community (TensorFlow Meets)
TensorFlow
 4 All About TensorFlow Code (Coding TensorFlow)
All About TensorFlow Code (Coding TensorFlow)
TensorFlow
 5 TensorFlow: an ML platform for solving impactful and challenging problems
TensorFlow: an ML platform for solving impactful and challenging problems
TensorFlow
 6 Keynote (TensorFlow Dev Summit 2018)
Keynote (TensorFlow Dev Summit 2018)
TensorFlow
 7 tf.data: Fast, flexible, and easy-to-use input pipelines (TensorFlow Dev Summit 2018)
tf.data: Fast, flexible, and easy-to-use input pipelines (TensorFlow Dev Summit 2018)
TensorFlow
 8 Eager Execution (TensorFlow Dev Summit 2018)
Eager Execution (TensorFlow Dev Summit 2018)
TensorFlow
 9 Machine Learning in JavaScript (TensorFlow Dev Summit 2018)
Machine Learning in JavaScript (TensorFlow Dev Summit 2018)
TensorFlow
 10 Training Performance: A user’s guide to converge faster (TensorFlow Dev Summit 2018)
Training Performance: A user’s guide to converge faster (TensorFlow Dev Summit 2018)
TensorFlow
 11 The Practitioner's Guide with TF High Level APIs (TensorFlow Dev Summit 2018)
The Practitioner's Guide with TF High Level APIs (TensorFlow Dev Summit 2018)
TensorFlow
 12 Distributed TensorFlow (TensorFlow Dev Summit 2018)
Distributed TensorFlow (TensorFlow Dev Summit 2018)
TensorFlow
 13 Debugging TensorFlow with TensorBoard plugins (TensorFlow Dev Summit 2018)
Debugging TensorFlow with TensorBoard plugins (TensorFlow Dev Summit 2018)
TensorFlow
 14 TensorFlow Lite (TensorFlow Dev Summit 2018)
TensorFlow Lite (TensorFlow Dev Summit 2018)
TensorFlow
 15 Searching Over Ideas (TensorFlow Dev Summit 2018)
Searching Over Ideas (TensorFlow Dev Summit 2018)
TensorFlow
 16 Reconstructing Fusion Plasmas (TensorFlow Dev Summit 2018)
Reconstructing Fusion Plasmas (TensorFlow Dev Summit 2018)
TensorFlow
 17 Nucleus: TensorFlow toolkit for Genomics (TensorFlow Dev Summit 2018)
Nucleus: TensorFlow toolkit for Genomics (TensorFlow Dev Summit 2018)
TensorFlow
 18 Open Source Collaboration (TensorFlow Dev Summit 2018)
Open Source Collaboration (TensorFlow Dev Summit 2018)
TensorFlow
 19 Swift for TensorFlow - TFiwS (TensorFlow Dev Summit 2018)
Swift for TensorFlow - TFiwS (TensorFlow Dev Summit 2018)
TensorFlow
 20 TensorFlow Hub (TensorFlow Dev Summit 2018)
TensorFlow Hub (TensorFlow Dev Summit 2018)
TensorFlow
 21 Applied AI at The Coca-Cola Company (TensorFlow Dev Summit 2018)
Applied AI at The Coca-Cola Company (TensorFlow Dev Summit 2018)
TensorFlow
 22 Real-World Robot Learning (TensorFlow Dev Summit 2018)
Real-World Robot Learning (TensorFlow Dev Summit 2018)
TensorFlow
 23 TensorFlow Extended (TFX) (TensorFlow Dev Summit 2018)
TensorFlow Extended (TFX) (TensorFlow Dev Summit 2018)
TensorFlow
 24 Project Magenta (TensorFlow Dev Summit 2018)
Project Magenta (TensorFlow Dev Summit 2018)
TensorFlow
 25 TensorFlow Dev Summit 2018 - Livestream
TensorFlow Dev Summit 2018 - Livestream
TensorFlow
 26 Introducing TensorFlow Lite (Coding TensorFlow)
Introducing TensorFlow Lite (Coding TensorFlow)
TensorFlow
 27 TensorFlow Dev Summit 2018 Highlights
TensorFlow Dev Summit 2018 Highlights
TensorFlow
 28 Jeff Dean, Head of AI at Google discusses the impact of ML (TensorFlow Meets)
Jeff Dean, Head of AI at Google discusses the impact of ML (TensorFlow Meets)
TensorFlow
 29 TensorFlow Mobile vs. TF Lite and More! #AskTensorFlow
TensorFlow Mobile vs. TF Lite and More! #AskTensorFlow
TensorFlow
 30 Using TensorFlow to enable research & production across many fields (TensorFlow Meets)
Using TensorFlow to enable research & production across many fields (TensorFlow Meets)
TensorFlow
 31 Teaching TensorFlow for Deep Learning at Stanford University (TensorFlow Meets)
Teaching TensorFlow for Deep Learning at Stanford University (TensorFlow Meets)
TensorFlow
 32 TensorFlow Lite for Android (Coding TensorFlow)
TensorFlow Lite for Android (Coding TensorFlow)
TensorFlow
 33 Using the tf.data API to build input pipelines (TensorFlow Meets)
Using the tf.data API to build input pipelines (TensorFlow Meets)
TensorFlow
 34 Training Models in the Cloud & the Benefits of AI Toolkits #AskTensorFlow
Training Models in the Cloud & the Benefits of AI Toolkits #AskTensorFlow
TensorFlow
 35 Execute operations immediately with TensorFlow's Eager Execution (TensorFlow Meets)
Execute operations immediately with TensorFlow's Eager Execution (TensorFlow Meets)
TensorFlow
 36 TensorFlow Lite for iOS (Coding TensorFlow)
TensorFlow Lite for iOS (Coding TensorFlow)
TensorFlow
 37 Get started with TensorFlow's High-Level APIs (Google I/O '18)
Get started with TensorFlow's High-Level APIs (Google I/O '18)
TensorFlow
 38 TensorFlow for JavaScript (Google I/O '18)
TensorFlow for JavaScript (Google I/O '18)
TensorFlow
 39 TensorFlow in production: TF Extended, TF Hub, and TF Serving (Google I/O '18)
TensorFlow in production: TF Extended, TF Hub, and TF Serving (Google I/O '18)
TensorFlow
 40 Get started with TensorFlow's High-Level APIs in 5 mins | Google I/O 2018
Get started with TensorFlow's High-Level APIs in 5 mins | Google I/O 2018
TensorFlow
 41 TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)
TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)
TensorFlow
 42 TensorFlow Lite for mobile developers (Google I/O '18)
TensorFlow Lite for mobile developers (Google I/O '18)
TensorFlow
 43 Advances in machine learning and TensorFlow (Google I/O '18)
Advances in machine learning and TensorFlow (Google I/O '18)
TensorFlow
 44 Distributed TensorFlow training (Google I/O '18)
Distributed TensorFlow training (Google I/O '18)
TensorFlow
 45 Classification using neural networks & ML regression models #AskTensorFlow
Classification using neural networks & ML regression models #AskTensorFlow
TensorFlow
 46 TensorFlow and Keras in R - Josh Gordon meets with J.J. Allaire (TensorFlow Meets)
TensorFlow and Keras in R - Josh Gordon meets with J.J. Allaire (TensorFlow Meets)
TensorFlow
 47 Focus on your experiment with TensorFlow Estimators (TensorFlow Meets)
Focus on your experiment with TensorFlow Estimators (TensorFlow Meets)
TensorFlow
 48 How to get started with AI/ML, retraining models, & more! #AskTensorFlow
How to get started with AI/ML, retraining models, & more! #AskTensorFlow
TensorFlow
 49 TensorFlow - the deep learning solution for mobile platforms (TensorFlow Meets)
TensorFlow - the deep learning solution for mobile platforms (TensorFlow Meets)
TensorFlow
 50 MiniGo: TensorFlow Meets Andrew Jackson (TensorFlow Meets)
MiniGo: TensorFlow Meets Andrew Jackson (TensorFlow Meets)
TensorFlow
 51 The growth of TensorFlow with added support for JS & Swift (TensorFlow Meets)
The growth of TensorFlow with added support for JS & Swift (TensorFlow Meets)
TensorFlow
 52 At the intersection of TensorFlow & nuclear physics (TensorFlow Meets)
At the intersection of TensorFlow & nuclear physics (TensorFlow Meets)
TensorFlow
 53 NVidia TensorRT: high-performance deep learning inference accelerator (TensorFlow Meets)
NVidia TensorRT: high-performance deep learning inference accelerator (TensorFlow Meets)
TensorFlow
 54 Try TensorFlow.js in your browser (Coding TensorFlow)
Try TensorFlow.js in your browser (Coding TensorFlow)
TensorFlow
 55 TensorFlow Hub: reusing machine learning modules (TensorFlow Meets)
TensorFlow Hub: reusing machine learning modules (TensorFlow Meets)
TensorFlow
 56 How to use TensorFlow in PyCharm (TensorFlow Tip of the Week)
How to use TensorFlow in PyCharm (TensorFlow Tip of the Week)
TensorFlow
 57 Training models faster with TensorFlow Hub (TensorFlow Meets)
Training models faster with TensorFlow Hub (TensorFlow Meets)
TensorFlow
 58 Prepare your dataset for machine learning (Coding TensorFlow)
Prepare your dataset for machine learning (Coding TensorFlow)
TensorFlow
 59 Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)
Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)
TensorFlow
 60 TFX: an end-to-end machine learning platform for TensorFlow (TensorFlow Meets)
TFX: an end-to-end machine learning platform for TensorFlow (TensorFlow Meets)
TensorFlow
⚡ AI Lesson Summary ✦ V3 skills 🛠 Hands-on

This video provides an overview of building machine learning infrastructure using TensorFlow, highlighting research and engineering problems. It is essential for ML engineers and researchers to understand these challenges and develop solutions. By watching this video, viewers can gain insights into building efficient ML pipelines and infrastructure.

Key Takeaways
  1. Watch the video to understand ML infra challenges
  2. Read the TFRT blog post for more information
  3. Explore the Inside TensorFlow playlist for related content
  4. Subscribe to the TensorFlow channel for updates
  5. Apply ML fundamentals to real-world problems
  6. Design and implement ML pipelines using TensorFlow
  7. Research and address engineering problems in ML infra
💡 Building ML infrastructure is a complex task that requires addressing research and engineering problems, and TensorFlow provides a framework to simplify this process.

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