In this course, you will learn about TensorFlow Extended (TFX). You will learn about ML engineering for production ML deployments with TFX, how TFX pipelines work, why we need metadata, distributed processing and components, model understanding and business reality, production ML pipelines with TensorFlow, Keynote, machine learning fairness, taking machine learning from research to production, data validation for machine learning, the production machine learning journey, Machine Learning Engineering for Production MLOps, and much more.
Overview
Syllabus
ML engineering for production ML deployments with TFX (TensorFlow Fall 2020 Updates).
What exactly is this TFX thing? (TensorFlow Extended).
How do TFX pipelines work? (TensorFlow Extended).
Why do I need metadata? (TensorFlow Extended).
Distributed Processing and Components (TensorFlow Extended).
Model Understanding and Business Reality (TensorFlow Extended).
TFX: Production ML pipelines with TensorFlow (TF World '19).
Day 2 Keynote (TF World '19).
Machine Learning Fairness: Lessons Learned (Google I/O'19).
TensorFlow Extended (TFX) Overview and Pre-training Workflow (TF Dev Summit '19).
TensorFlow Extended (TFX) Post-training Workflow (TF Dev Summit '19).
TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19).
TFX: Production ML with TensorFlow in 2020 (TF Dev Summit '20).
Taking Machine Learning from Research to Production • Robert Crowe • GOTO 2019.
SysML 19: Martin Zinkevich, Data Validation for Machine Learning.
Continuous retraining with TFX and Beam.
ML Summit: Predict | ML Engineering for Production ML Deployments.
From Experimentation to Products: The Production Machine Learning Journey • Robert Crowe • GOTO 2021.
Machine Learning Engineering for Production (MLOps).
Taught by
TensorFlow
