MLOps and TFX with Beam and Dataflow

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)

Deploying production ML models with TensorFlow Serving overview

TensorFlow Serving client examples

How to customize TensorFlow Serving

TensorFlow Serving performance optimization

Advanced features on TensorFlow Serving

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 Deployment [Google #ML Summit]

From Experimentation to Products: The Production Machine Learning Journey • Robert Crowe • GOTO 2021

Machine Learning Engineering for Production (MLOps)

Beam Summit 2021 - ML Inference at scale, easy as learning your 5 times table

Does your app use ML? Make it a product with TFX | Session

Manage MLOps and deploy Machine Learning to production with the new and improved TFX

Adapting to Change: How Machine Intelligences Adapt to a Changing World • Robert Crowe • GOTO 2022