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Easy Learning with Mastering LLM Evaluation: Build Reliable Scalable AI Systems
IT & Software > Other IT & Software
3 h
£14.99 £12.99
3.9 ★★★★★
6559 students

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Language: English

Reliable & Scalable AI: Mastering Large Language Model Evaluation

What you will learn:

  • Master the complete lifecycle of LLM evaluation, from initial concept to production monitoring.
  • Accurately identify and classify common LLM output errors.
  • Develop and implement efficient error analysis and annotation procedures.
  • Construct automated evaluation pipelines utilizing code-based and LLM-judge metrics.
  • Evaluate various LLM architectures including RAG, multi-turn agents, and multi-modal systems.
  • Implement continuous monitoring dashboards with detailed trace data, alerts, and CI/CD integration.
  • Optimize model resource usage and cost with intelligent routing, fallback mechanisms, and caching.
  • Integrate human-in-the-loop review systems for continuous feedback and quality assurance.

Description

Elevate your AI development with our comprehensive guide to Large Language Model (LLM) evaluation. This course equips you with the practical skills and strategic insights to build AI applications that are not only intelligent but also reliable, efficient, and cost-effective. We move beyond theory, diving deep into hands-on labs and real-world case studies, enabling you to master the art and science of evaluating LLM outputs throughout the entire development lifecycle—from initial prototype to full-scale production.

Learn how to design, implement, and maintain robust evaluation frameworks, tackling common challenges such as hallucinations, inconsistencies, and unexpected behaviors. Discover effective annotation strategies, synthetic data generation techniques, and the creation of automated evaluation pipelines. Master error analysis, insightful observability instrumentation, and the crucial aspects of cost optimization through strategic routing and vigilant monitoring. This isn't just about theoretical understanding; you'll build practical test suites for complex systems including Retrieval Augmented Generation (RAG) systems, multi-modal agents, and intricate multi-step LLM pipelines.

We'll guide you through the process of integrating human-in-the-loop (HITL) evaluation and continuous feedback loops to ensure your system continuously learns and improves. Develop expertise in annotation taxonomy, inter-annotator agreement, and the art of constructing collaborative evaluation workflows across teams. Finally, discover how to connect evaluation metrics directly to key business performance indicators (KPIs) like customer satisfaction (CSAT), conversion rates, and time-to-resolution—demonstrating the direct ROI of your improved LLM performance. This course empowers AI engineers, product managers, MLOps specialists, and data scientists to confidently build and deploy trustworthy, measurable, and scalable LLM applications.

This course is tailored for:

  • AI engineers creating and maintaining LLM-powered systems
  • Product managers focusing on AI quality and safety
  • MLOps and platform engineers aiming to scale evaluation processes
  • Data scientists concentrating on AI reliability and error analysis

Gain the competitive edge in today's AI-driven landscape by mastering scalable, automated, and cost-efficient LLM evaluation. Enroll now and transform your AI development!

Curriculum

Fundamentals of LLM Evaluation

This section lays the groundwork for effective LLM evaluation. You'll understand the crucial role evaluation plays in building robust AI, explore the challenges unique to LLMs, and learn to visualize the entire evaluation lifecycle. We cover observability and instrumentation basics, introduce the concept of error analysis, and conclude with practical labs applying these core principles.

Systematic Error Analysis

Delve into the art of systematic error analysis. Learn to leverage synthetic data for bootstrapping, master annotation and categorization techniques, and transform errors into actionable insights. We'll examine common pitfalls and guide you through a lab where you build your own error tracking system.

Implementing Effective Evaluations

This section focuses on practical implementation. Learn to design and choose appropriate evaluation metrics, understand the differences between individual and system-level evaluations, and master dataset structuring. A key lab focuses on building a complete evaluation pipeline, consolidating your learning.

Collaborative Evaluation Practices

Collaboration is key. This section teaches you how to establish efficient team-based evaluation workflows, measure inter-annotator agreement, and foster consensus. A dedicated lab focuses on a practical alignment workshop to solidify your collaboration skills.

Architecture-Specific Strategies

This section dives into architecture-specific evaluation strategies. You'll learn to evaluate Retrieval Augmented Generation (RAG) systems, multi-step pipelines, and multi-modal models effectively. A dedicated lab lets you build test suites for various architectures.

Monitoring & Continuous Evaluation

Learn to establish continuous monitoring for your LLMs. Discover techniques for tracing and observability, implementing CI/CD evaluation gates, conducting A/B testing, and designing safety and guardrails. Build a monitoring dashboard in the provided lab.

Human-in-the-Loop Evaluation

Explore the power of human-in-the-loop evaluation. Learn strategic sampling techniques, optimize reviewer interfaces, and build a continuous feedback system using provided labs and templates.

Cost Optimization in Eval Workflows

Master cost optimization in your evaluation workflows. Learn to balance value and spend, implement model routing strategies, and complete a lab project focused on practical cost optimization techniques.