Production Machine Learning Systems Course Syllabus

A compact yet powerful course showing how to scale ML into production on GCP with strong architectural principles and hands-on pipeline labs. This course spans approximately 7 hours of content, structured into focused modules that emphasize practical design and implementation of production-grade machine learning systems using Google Cloud Platform and TensorFlow. Each module combines foundational concepts with real-world labs to prepare engineers for deploying scalable, maintainable ML systems.

Module 1: Architecting Production ML Systems

Estimated time: 4 hours

• Core components of production ML: data ingestion and preprocessing
• Feature extraction and transformation pipelines
• Model lifecycle management on Vertex AI
• Designing for model serving and monitoring
• Hands-on: Building a structured-data pipeline using Vertex AI

Module 2: Designing Adaptable Systems

Estimated time: 3 hours

• Understanding static vs. dynamic ML pipelines
• Handling concept drift in production environments
• Strategies for system robustness and error handling
• Using TensorFlow Data Validation (TFDV) to detect data anomalies
• Hands-on: Lab exercise on detecting and reacting to data shifts with TFDV

Module 3: Training and Inference Paradigms

Estimated time: 2 hours

• Static, dynamic, and continuous training workflows
• Batch vs. online inference patterns
• Real-world deployment scenarios on GCP
• Trade-offs between latency, cost, and accuracy

Module 4: Scalable Model Management with Vertex AI and TensorFlow

Estimated time: 2 hours

• Integrating Vertex AI with TensorFlow for model training
• Distributed training using custom estimators
• Model versioning and deployment best practices
• Scaling model inference workloads

Module 5: Managing Data Challenges in Production

Estimated time: 2 hours

• Data extraction and preprocessing at scale
• Feature engineering for production pipelines
• Monitoring data quality and pipeline health
• Handling schema changes and missing data

Module 6: Final Project

Estimated time: 3 hours

• Design and implement an end-to-end ML pipeline on GCP
• Incorporate data validation and concept drift detection
• Deploy a model using Vertex AI with monitoring in place

Prerequisites

• Familiarity with TensorFlow and building ML models
• Basic understanding of Google Cloud Platform (GCP) services
• Experience with ML fundamentals such as training, evaluation, and inference

What You'll Be Able to Do After

• Architect production-grade ML pipelines on GCP
• Design systems resilient to concept drift and data anomalies
• Implement scalable training and serving workflows
• Use Vertex AI and TensorFlow tools effectively in real-world scenarios
• Monitor and maintain ML systems for long-term reliability