LLM Optimization & Evaluation

Coursera via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

Found in

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

Learn the complete lifecycle of LLM optimization and evaluation through hands-on experience with production-ready techniques. This comprehensive specialization equips you with essential skills to evaluate, optimize, and deploy large language models effectively. You'll learn to engineer features for ML models, implement rigorous statistical testing for LLM performance, diagnose and fix hallucinations through log analysis, optimize both computational costs and database performance, and build robust safety testing frameworks. The program progresses from foundational ML concepts through advanced MLOps practices, covering experiment tracking with tools like DVC and W&B, automated cloud workflows, data pipeline management with Apache Airflow, and product development workflows including requirements documentation and user acceptance testing. Through practical projects, you'll analyze LLM spend reports to reduce operational costs, implement value-stream mapping to streamline ML pipelines, create comprehensive testing suites with mutation testing, and develop operational runbooks for production systems. Whether you're optimizing SQL queries for vector search, conducting A/B tests for model improvements, or building automated monitoring systems, this specialization provides the technical depth and practical experience needed to excel in LLM engineering roles.

Syllabus

Course 1: Engineer Features and Evaluate Models for Production
Course 2: Optimize Deep Learning: Tune PyTorch Models
Course 3: Evaluate & Optimize LLM Performance
Course 4: Analyze Logs: Fix LLM Hallucinations
Course 5: Evaluate LLMs: Test and Prove Significance
Course 6: Optimize SQL: Build Fast Data Pipelines
Course 7: Safeguard LLM Outputs: Test and Evaluate
Course 8: Track and Evaluate ML Model Experiments
Course 9: Automate Cloud Workflows with Python Scripting
Course 10: Automate Data Pipelines: Schema Evolution
Course 11: Develop and Evaluate LLM Features Effectively
Course 12: Document and Evaluate LLM Prompting Success
Course 13: Optimize LLM Costs & Streamline Processes

Courses

0 reviews
2 hours 5 minutes
View details

Automate Data Pipelines: Schema Evolution is an intermediate course designed for data engineers, analysts, and developers looking to build robust, failure-resistant data workflows. In today's dynamic data landscape, pipelines often break when source data structures change unexpectedly—a problem known as schema drift. This course tackles that challenge head-on, teaching you how to design and automate data pipelines that can gracefully handle schema evolution using Apache Airflow. You will gain hands-on experience designing, building, and scheduling complex data pipelines (DAGs) that automate ETL processes from extraction to loading. The curriculum places a strong emphasis on creating idempotent workflows that detect and adapt to schema changes, ensuring data integrity and preventing costly failures. Through practical labs and real-world case studies from companies like Uber and BharatPe, you will implement data validation checks and build comprehensive monitoring and alerting systems. By the end of this course, you will be equipped to create resilient, scalable, and fully automated data pipelines that are built to withstand the complexities of real-world data environments.
0 reviews
2 hours 43 minutes
View details

Develop and Evaluate LLM Features Effectively is an intermediate course designed for product managers, QA professionals, and developers working on AI-powered features. This course teaches you how to prevent common LLM failures—like providing illegal advice or having bizarre conversations—by implementing professional product management practices. You will learn to create a Product Requirements Document (PRD), establishing a single source of truth that defines scope, MVP features, and success metrics for an LLM product. You will then shift from planning to validation, learning to build and execute a User Acceptance Testing (UAT) plan based on testable user stories. Through hands-on activities, you will draft a PRD for an HR chatbot and test a simulated feature for functional gaps and dangerous edge cases. By the end of this course, you will be able to ensure that the features you build are not only technically sound but also safe, effective, and aligned with your original vision.
0 reviews
2 hours 46 minutes
View details

Engineer Features and Evaluate Models for Production is an intermediate course for machine learning practitioners and data scientists who are ready to move beyond notebooks and build production-grade ML systems. Getting a model to work once is easy; making it reliable, reproducible, and efficient in production is the real challenge. This course provides the engineering discipline to bridge that gap. You will learn to build robust, reproducible feature engineering pipelines using scikit-learn's ColumnTransformer to handle mixed data types—numeric, categorical, and text—in a single, elegant workflow. Then, you will move beyond simple accuracy scores and learn to evaluate experiments like a seasoned MLOps professional. Using TensorBoard, you will inspect training and validation curves to diagnose issues such as overfitting, analyze performance trade-offs, and make data-driven decisions. The course culminates in a comprehensive Feature Engineering and Evaluation Report, where you will apply your skills to select a production-ready model. By the end, you will not only be building models, but also be capable of engineering reliable, efficient, and production-worthy ML systems.
0 reviews
3 hours 13 minutes
View details

Evaluate LLMs: Test and Prove Significance is an intermediate course for ML engineers, AI practitioners, and data scientists tasked with proving the value of model updates. When making high-stakes deployment decisions, a simple accuracy score is not enough. This course equips you with the statistical methods to rigorously validate LLM performance improvements. You will learn to quantify uncertainty by calculating and interpreting confidence intervals, and to prove whether changes are meaningful by conducting formal hypothesis tests like the Chi-Square test. Through hands-on labs using Python libraries like SciPy and Matplotlib, you will analyze model outputs, test for statistical significance, and create compelling visualizations with error bars that clearly communicate your findings to stakeholders. By the end of this course, you will be able to move beyond subjective "it seems better" evaluations to confidently state, "we can prove it's better," ensuring every deployment decision is backed by sound statistical evidence.
0 reviews
3 hours 37 minutes
View details

When a production chatbot starts giving incorrect answers, how do you find the problem and fix it? "Analyze Logs: Fix LLM Hallucinations" is an intermediate course that equips AI practitioners, ML engineers, and data analysts with the essential skills for debugging production LLMs. Go beyond theory and learn the systematic, data-driven workflow that professionals use to solve the critical problem of AI hallucinations. You will utilize the Pandas library to analyze production logs, segment user behavior by intent, and calculate key business metrics, such as 7-day retention, to identify which user journeys are failing. Then, you will perform a root cause analysis, correlating different error types with retrieval system performance to pinpoint exactly why your model is failing. Finally, you will learn to translate your analytical findings into a clear, actionable engineering brief that drives real solutions. This course will empower you to transition from merely observing AI failures to expertly diagnosing and resolving them.
0 reviews
1 hour 25 minutes
View details

Automate Cloud Workflows with Python Scripting is an intermediate course designed for developers, ML engineers, and data scientists seeking to eliminate manual, error-prone cloud tasks. Modern ML workflows often involve multiple complex steps—provisioning a GPU, running a training job, and saving the model—all of which are inefficient to perform by hand. This course teaches you how to automate this entire process from end to end using Python. You will learn to write powerful scripts that programmatically manage cloud resources, execute multi-step computational jobs, and ensure data persistence. Through hands-on labs, you will master using Python’s argparse library to create flexible, reusable scripts configurable from the command line, and you’ll apply Infrastructure as Code (IaC) principles to build scalable, resilient, and cost-effective solutions. By the end of this course, you will be equipped to transform your manual cloud processes into robust, automated pipelines ready for production.
0 reviews
1 hour 52 minutes
View details

Document and Evaluate LLM Prompting Success is an intermediate course for ML engineers and AI practitioners responsible for the stability and performance of live LLM systems. Moving an LLM from a cool prototype to a reliable production service requires more than just clever prompting—it demands operational discipline. This course provides the framework for that discipline. You will learn to create professional-grade operational documentation, authoring a step-by-step run-book for managing critical system tasks like a vector index update, complete with validation checks and rollback procedures. You will also move from prompt artistry to prompt science, learning to systematically evaluate and A/B test prompt patterns. By analyzing the trade-offs between response quality, consistency, and token cost, you will make data-driven decisions that ensure both performance and efficiency. The course culminates in creating an LLMOps Production-Readiness Toolkit, equipping you to manage and optimize production AI systems effectively.
0 reviews
3 hours 59 minutes
View details

You've integrated a powerful Large Language Model (LLM) into your application. The initial results are impressive, and your team is excited. But then the hard questions start. Is the new prompt really better than the old one, or does it just "feel" better? How do you prove to stakeholders that switching from GPT-3.5 to GPT-4 is worth the extra cost? When you have two models that give slightly different answers, how do you decide which one is objectively superior? After completing this course, you will have the confidence to lead your team in making smart, evidence-based decisions that measurably improve your AI applications. Ready to Become an LLM Expert? It's time to bring scientific rigor to the art of AI. Enroll in Evaluate & Optimize LLM Performance and gain the essential skills to build, validate, and perfect the next generation of language models.
0 reviews
1 hour 47 minutes
View details

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.
0 reviews
3 hours 27 minutes
View details

As AI models like Google's Gemini have shown, even the most advanced systems can have spectacular safety failures, leading to brand damage and a loss of user trust. "Safeguard LLM Outputs: Test and Evaluate" is an intermediate course for developers and ML engineers who need to move beyond functional testing and build truly trustworthy AI. This course teaches you the rigorous, adversarial testing methodologies that professional AI Red Teams use to secure high-stakes applications. You will learn to translate abstract safety policies into concrete, automated behavioral tests using pytest, designing adversarial prompts to systematically probe for weaknesses. Then, you will master the practice of "testing your tests" by using mutation testing frameworks like mutmut to find and eliminate hidden gaps in your safety net. By the end of this course, you will be able to not only ensure your LLM behaves safely but also prove that the tests verifying that safety are themselves comprehensive and robust.
0 reviews
2 hours 57 minutes
View details

Track & Evaluate ML Model Experiments is an essential intermediate course for Machine Learning Engineers, Data Scientists, and MLOps practitioners aiming to elevate their process from ad-hoc scripting to a systematic, professional discipline. If you have ever faced the "it worked on my machine" problem or struggled to reproduce a great result from weeks ago, this course will provide you with the foundational MLOps practices to build a truly auditable and collaborative workflow. The primary goal is to empower you to manage the entire experiment lifecycle with confidence, ensuring that every model you build is reproducible, traceable, and ready for the rigors of production. Throughout this course, you will get hands-on with industry-standard tools. You will learn to use Data Version Control (DVC) to version datasets and models with the same rigor you apply to code, creating a single source of truth for your team. You will then instrument training scripts with Weights & Biases (W&B) to automatically log every hyperparameter, metric, and artifact to a centralized, interactive dashboard. Finally, you will master a structured evaluation framework to make defensible model selections, moving beyond a single F1 score to balance predictive performance with critical operational constraints like latency and memory usage. Upon completion, you will have a complete toolkit for managing the ML lifecycle with clarity and precision. For learners interested in applying these MLOps skills to the next frontier, this course serves as a perfect foundation for more advanced topics, such as those covered in the LLM Engineering That Works: Prompting, Tuning & Retrieval course.
0 reviews
1 hour 44 minutes
View details

In today's data-driven landscape, the difference between slow analytics and lightning-fast insights often comes down to how efficiently you can transform raw data into meaningful summaries and optimize query performance. This Short Course was created to help data analysts transform their SQL skills from writing basic queries to building production-ready, scalable data pipelines. By completing this course you'll be able to create parameterized SQL scripts for daily data materialization, systematically diagnose performance bottlenecks that slow down analytical workflows, and build automated data transformation pipelines that previously required senior engineering support—making you an indispensable asset to any data-driven organization. By the end of this course, you will be able to: Master creating parameterized SQL scripts for daily data materialization Systematically diagnose performance bottlenecks in analytical workflows Build repeatable ETL processes using advanced SQL techniques like CTEs and window functions Develop diagnostic expertise to interpret execution plans and optimize query performance through strategic indexing and query restructuring Confidently build automated data transformation pipelines and troubleshoot performance issues independently This course is unique because it empowers data analysts to master the critical skills of building repeatable ETL processes and developing diagnostic expertise that bridges the gap between basic SQL knowledge and production-level data engineering capabilities. To be successful in this course, you should have a background in basic SQL querying, fundamental database concepts, and experience working with data analysis workflows.