Python Machine Learning in Production

Overview

“Python Machine Learning in Production” addresses the critical gap between machine learning experimentation and production deployment. This comprehensive guide teaches you how to build robust, scalable ML systems that work reliably in real-world environments.

What You’ll Learn

• MLOps Fundamentals: Establish sustainable ML development and deployment workflows
• Model Serving: REST APIs, batch processing, and real-time inference systems
• Infrastructure: Container orchestration, auto-scaling, and cloud deployment
• Monitoring & Observability: Model performance tracking and drift detection
• Data Engineering: Pipeline design, feature stores, and data validation
• Production Optimization: Latency reduction, throughput optimization, and cost management

Key Features

• End-to-End Projects: Complete ML systems from development to monitoring
• Cloud Platform Integration: AWS, GCP, and Azure deployment strategies
• Modern Tools: Kubernetes, Docker, MLflow, Kubeflow, and more
• Performance Engineering: Optimization techniques for production workloads

Table of Contents

1. Production ML Fundamentals and MLOps Principles
2. Model Development and Experimentation Workflows
3. Data Pipeline Architecture and Feature Engineering
4. Model Serving: Batch, Streaming, and Real-time
5. Containerization and Orchestration for ML
6. Model Monitoring and Performance Tracking
7. A/B Testing and Model Evaluation in Production
8. Scaling ML Systems: Performance and Cost Optimization
9. Security and Compliance in ML Systems
10. Advanced MLOps: Multi-Model Systems and Edge Deployment

About the Author

Dr. Amanda Chen is a Principal ML Engineer at a Fortune 100 technology company, where she leads the development of large-scale recommendation systems serving millions of users. She holds a PhD in Computer Science and has published extensively on production ML systems.