AI Engineering Mastery
Architect robust AI systems with production-grade machine learning engineering practices covering MLOps, model deployment, and scalable training pipelines.
Start Learning AI EngineeringArchitect robust AI systems with production-grade machine learning engineering practices covering MLOps, model deployment, and scalable training pipelines.
Start Learning AI EngineeringAI Engineering combines data science, software engineering, and operations to build, deploy, and monitor production AI systems. This guide provides practical knowledge on creating scalable machine learning pipelines, model versioning, and continuous training systems.
Learn to manage machine learning lifecycle with MLOps frameworks, implement model monitoring, and build feature stores for production grade ML applications.
9+ Chapters
Structured Learning
Master these fundamental areas of AI system development
Build end-to-end machine learning pipelines with automated data preprocessing, model training, and continuous evaluation in production environments.
Learn to containerize AI models using Docker/TensorFlow Serving, and implement scalable model serving architectures with load balancing.
Implement drift detection, performance tracking, and error analysis for deployed machine learning models in production.
Expand your expertise with specialized machine learning engineering techniques
Implement privacy-preserving training across decentralized devices and servers while keeping data localized.
Optimize inference performance with quantization, pruning, and model compression techniques for edge deployment.
Apply engineering principles through these real-world project templates
Deploy TensorFlow models for embedded vision with optimized inference on edge devices using ONNX runtime.
Build scalable chatbot deployment architecture with conversation memory, rate limiting, and context window management.