Production Machine Learning Systems

Overview

This book covers the complete ML lifecycle from research to production, including model development, MLOps strategies, deployment patterns, monitoring systems, and scaling considerations.

What You'll Learn

Chapter 1: Introduction to ML Systems

• Understanding the ML lifecycle
• Key challenges in production ML
• Architecture patterns and best practices
• Setting up your development environment

Chapter 2: Model Development & Experimentation

• Experiment tracking and versioning
• Feature engineering at scale
• Model selection and validation
• Hyperparameter optimization strategies

Chapter 3: MLOps & Deployment Strategies

• CI/CD for machine learning
• Containerization with Docker
• Kubernetes for ML workloads
• Blue-green and canary deployments

Chapter 4: Monitoring & Maintenance

• Model performance monitoring
• Data drift detection
• Alert systems and dashboards
• Automated retraining pipelines

Chapter 5: Scaling Considerations

• Distributed training strategies
• Model serving at scale
• Cost optimization techniques
• Multi-region deployments

Code Examples

Throughout the book, you'll find practical code examples in Python, covering:

# Example: Setting up experiment tracking
import mlflow
import mlflow.sklearn

with mlflow.start_run():
    # Train your model
    model = train_model(X_train, y_train)

    # Log metrics and model
    mlflow.log_metric("accuracy", accuracy_score(y_test, predictions))
    mlflow.sklearn.log_model(model, "model")

Prerequisites

• Python programming experience
• Basic understanding of machine learning concepts
• Familiarity with cloud platforms (AWS, GCP, or Azure)
• Docker and Kubernetes basics (helpful but not required)

Resources

• GitHub Repository: Complete code examples and exercises
• Sample Datasets: Real-world datasets for hands-on practice
• Video Tutorials: Supplementary video content
• Community Forum: Connect with other readers

This book is currently in progress. Expected completion: 2024