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