GCP-PMLS: Production Machine Learning Systems

Module 1: Introduction to Production ML Systems

• Production challenges
• Static vs. dynamic vs. continuous training
• Batch vs. online processing

Module 2: Inference in Production

• Static inference (precomputed)
• Dynamic inference (on-demand)
• Latency and throughput considerations

Module 3: TensorFlow Abstraction Layers

• TF 2.x ecosystem
•  Estimators, Keras, and custom loops
• When to use which abstraction

Module 4: Distributed Training Fundamentals

• Why distribute training
• Data parallelism vs. model parallelism
• MirroredStrategy, TPUStrategy, MultiWorkerMirroredStrategy

Module 5: Custom Estimators for Distributed Training

• Writing custom estimators
• Model functions and input functions
• Lab: Distributed training with custom estimators

Module 6: Production Pipeline Architecture

• Continuous training pipelines
• Model versioning and rollback
• Monitoring and alerting

Module 7: Challenge Lab (Skills Badge)

• Jump directly to a challenge lab
• Demonstrate production ML skills without completing all modules