Optimize LLM Costs & Streamline Processes is an intermediate course for machine learning practitioners and AI professionals looking to bridge the gap between technical execution and operational excellence. You will learn two critical, in-demand skills: cost optimization for Large Language Models (LLMs) and process streamlining for ML workflows. First, you will dive into the financial side of MLOps, learning to dissect compute-spend reports, pinpoint the models driving up costs, and propose concrete technical optimizations like INT8 quantization to make a measurable financial impact. Next, you will master the principles of lean management by applying Value-Stream Mapping (VSM) to complex ML pipelines. Through hands-on labs using tools like Miro and spreadsheets, you will learn to visualize workflows, identify hidden waste like manual bottlenecks and wait times, and design streamlined, automated future-state processes. By the end of this course, you will be equipped to not only build powerful models but also to deploy and manage them in a way that is cost-efficient, fast, and aligned with business goals.

Optimize LLM Costs & Streamline Processes

Optimize LLM Costs & Streamline Processes
This course is part of LLM Optimization & Evaluation Specialization

Instructor: LearningMate
Access provided by Lam Research
Recommended experience
What you'll learn
Optimize LLM costs by analyzing spend reports and streamline ML pipelines using value-stream mapping to boost efficiency and reduce cycle times.
Skills you'll gain
Tools you'll learn
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December 2025
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There are 2 modules in this course
In this module, you will learn to translate financial data from compute-spend reports into actionable technical strategies. You will dissect these reports to pinpoint high-cost models, explore optimization techniques like quantization, and learn to propose solutions that directly impact the bottom line.
What's included
2 videos1 reading2 assignments
This module introduces lean management principles through Value-Stream Mapping (VSM). You will learn to visualize an end-to-end ML pipeline, systematically identify and classify waste, and design a streamlined future-state process that leverages automation to shorten cycle times and increase efficiency.
What's included
2 videos1 reading2 assignments
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