This module provides ML engineers and practitioners with the operational discipline needed to transition LLM prototypes into reliable production services. You will move from "prompt artistry" to prompt science, learning to systematically evaluate and A/B test prompt patterns while balancing response quality, consistency, and token costs. The curriculum focuses on creating professional-grade operational documentation, such as step-by-step run-books for vector index updates, complete with validation checks and rollback procedures. By developing an LLMOps Production-Readiness Toolkit, you will gain the expertise to make data-driven decisions that ensure both high performance and cost efficiency in live AI systems.

This module bridges technical execution and operational excellence for ML practitioners. You will master two critical pillars: cost optimization and process streamlining. First, you’ll dive into MLOps financials, learning to dissect compute-spend reports and implement technical optimizations like INT8 quantization to reduce overhead. Next, you will apply Value-Stream Mapping (VSM) to ML pipelines using tools like Miro to visualize workflows and eliminate manual bottlenecks. By the end, you’ll be equipped to design automated, future-state processes that ensure your LLM deployments are fast, cost-efficient, and business-aligned.

Step into the role of a senior analyst tasked with overhauling an underperforming and costly LLM chatbot. In this module, you will conduct a comprehensive 360-degree audit to diagnose core issues across product, performance, and process. You’ll define KPIs, perform a feature gap-analysis, run experiments to optimize prompt strategies, and use value-stream mapping and cost modeling to identify savings and efficiencies, delivering actionable recommendations to improve performance, reduce costs, and create a high-value asset for your portfolio.