AI Evals For Engineers & PMs on Maven equips developers and product managers with practical skills to test, debug, and improve AI systems—especially LLMs with subjective outputs. Led by experts Hamel Husain (ex-Hugging Face) and Shreya Shankar (ex-OpenAI), this flipped-classroom course emphasizes real-world workflows over theory.
Course Structure and Features
The program runs over several weeks with 10+ live office hours, 77 lessons, and 4 optional coding assignments. Students get lifetime access to materials, a 150+ page course reader, Discord community, and 6 months of an exclusive AI Eval Assistant tool.
Key elements include:
Professionally recorded lectures (2-3 hrs/week).
Guest workshops from experts like Simon Willison and Eugene Yan.
Focus on instrumentation, data annotation, agentic systems, multi-modal evals, and safety guardrails.
What You’ll Learn
Core topics address common AI challenges:
Systematic error analysis (open/axial coding) to prioritize fixes.
Building trustworthy LLM-as-judge and code-based evals.
Synthetic data for bootstrapping without real users.
RAG evaluation for retrieval accuracy and multi-step pipeline debugging.
This hands-on approach helps teams measure prompt changes, automate testing, and avoid regressions.
Benefits and Highlights
Proven Impact: Top-grossing Maven course; trained 2,000+ from OpenAI, Anthropic, Google.
Community Support: Lifetime Discord access and office hours for ongoing help.
Practical Tools: Vendor deep-dives and CI/CD eval gates.
Guarantee: Full refund until halfway point.
Potential Drawbacks
Pricing isn’t listed publicly (often cohort-based, up to $3,500 per reviews); requires commitment to office hours. Best for those shipping production AI, not beginners.
Who It’s For
Ideal for:
Engineers/PMs building LLM apps needing reliable metrics.
Teams manual-checking outputs or lacking eval strategy.
AI leaders prioritizing issues scientifically.
Learn directly from Hamel & Shreya
Hamel Husain
ML Engineer with 20 years of experience.
Hamel Husain is a ML Engineer with over 20 years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies build AI products.
Shreya Shankar
ML Systems Researcher Making AI Evaluation Work in Practice
Shreya builds open-source systems for AI-powered data processing. She is a final-year PhD at UC Berkeley. Shreya created DocETL, an open-source system for analyzing unstructured text at scale. DocETL has been deployed across journalism, law, medicine, policy, finance, and urban planning. Her research has been published at top computer science venues including VLDB, SIGMOD, and UIST (including a Best Paper award). Before her PhD, Shreya worked as a machine learning and data engineer at startups. She holds a BS in Computer Science from Stanford University.
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Product Details: https://tinyurl.com/yc79ukuu
File size: 8.6GB
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