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Machine Learning Engineering for Production (MLOps)
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Classroom Contents
TensorFlow Extended
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- 1 ML engineering for production ML deployments with TFX (TensorFlow Fall 2020 Updates)
- 2 What exactly is this TFX thing? (TensorFlow Extended)
- 3 How do TFX pipelines work? (TensorFlow Extended)
- 4 Why do I need metadata? (TensorFlow Extended)
- 5 Distributed Processing and Components (TensorFlow Extended)
- 6 Model Understanding and Business Reality (TensorFlow Extended)
- 7 TFX: Production ML pipelines with TensorFlow (TF World '19)
- 8 Day 2 Keynote (TF World '19)
- 9 Machine Learning Fairness: Lessons Learned (Google I/O'19)
- 10 TensorFlow Extended (TFX) Overview and Pre-training Workflow (TF Dev Summit '19)
- 11 TensorFlow Extended (TFX) Post-training Workflow (TF Dev Summit '19)
- 12 TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19)
- 13 TFX: Production ML with TensorFlow in 2020 (TF Dev Summit '20)
- 14 Taking Machine Learning from Research to Production • Robert Crowe • GOTO 2019
- 15 SysML 19: Martin Zinkevich, Data Validation for Machine Learning
- 16 Continuous retraining with TFX and Beam
- 17 ML Summit: Predict | ML Engineering for Production ML Deployments
- 18 From Experimentation to Products: The Production Machine Learning Journey • Robert Crowe • GOTO 2021
- 19 Machine Learning Engineering for Production (MLOps)
