Model Development and Optimization for Production (With Implementation)

...covered from a system design perspective.

Part 9 of the MLOps and LLMOps crash course is now available, which builds on the modeling phase we discussed in Part 8, while also covering fine-tuning and compression (with implementation).

Read here: MLOps and LLMOps crash course Part 9 →

When developing ML models for production systems, the goal is not just to maximize accuracy on a leaderboard or validation set; it's to build models that perform well and run efficiently under real-world constraints.

Efficient model development means designing and training models with an eye on these operational concerns from the start.

We'll cover:

• Model development fundamentals

• Phases or model development and deployment

• Debugging model training

• Optimization: Hyperparameter tuning

Just like all our past series on MCP, RAG, and AI Agents, this series is both foundational and implementation-heavy, walking you through everything that a real-world ML system entails:

A conceptual ML system in production, depicting the share of ML model codes in the complete project

• Part 1 covered foundations and MLOps system principles →

• Part 2 covered ML system lifecycle with hands-on project →

• Part 3 covered reproducibility and versioning for ML systems →

• Part 4 also covered reproducibility and versioning for ML systems →

• Part 5 covered data and pipeline engineering →

• Part 6 covered building scalable data pipelines →

• Part 7 covered Spark, and orchestration + workflow management →

• Part 8 covered the modeling phase of the MLOps lifecycle from a system perspective →

This MLOps and LLMOps crash course provides a thorough explanation and systems-level thinking to build AI models for production settings.

Just like the MCP crash course, each chapter will clearly explain necessary concepts, provide examples, diagrams, and implementations.

Thanks for reading!