Maven cohort course
Hamza Farooq on Maven
Beginner to intermediate
Use this when you want Agentic AI for Product Managers's material for agentic ai and related AI skills.
AI educator
Hamza Farooq on Maven
Useful for PMs who need to design, evaluate, and ship reliable AI systems beyond impressive demos.
Start with: Use the course to evaluate one AI product opportunity and define what reliability would mean before implementation.
Maven cohort course
Beginner to intermediate
Use this when you want Agentic AI for Product Managers's material for agentic ai and related AI skills.
Product managers, AI product leaders, founders should start here when they need agentic ai, ai product strategy, evals, and production ai. The strongest fit is a learner who wants material in these formats: maven cohort course, case studies.
Use the course to evaluate one AI product opportunity and define what reliability would mean before implementation. After that, open one related resource below and write down the exact workflow, concept, or implementation pattern you want to apply.
Useful for PMs who need to design, evaluate, and ship reliable AI systems beyond impressive demos. Use this profile when you are comparing educators by topic, level, format, and practical usefulness rather than browsing random AI content.
Compare the skill coverage, the starting recommendation, the educator's own resources, and any videos when available. If you need agentic ai, search the directory for that skill and shortlist three profiles before committing to a course, book, or playlist.
| Resource | Kind | Level | Use when |
|---|---|---|---|
|
AI SDK v6 Crash Course
Matt Pocock
|
Workshop | Intermediate | You want a structured AI SDK v6 course that covers model choice, text and object generation, UI streams, agents, persistence, context engineering, evals, and advanced app patterns. |
|
The AI Engineer Roadmap
Matt Pocock
|
Free tutorial | Beginner to intermediate | You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps. |
|
LLM Evals
Hamel Husain
|
Guide | Intermediate | Your AI app needs quality checks before users see it. |
|
Evaluating AI Agents
DeepLearning.AI
|
Short course | Intermediate | You need to test, trace, and improve agent workflows instead of judging only single LLM responses. |
|
Building and Evaluating Advanced RAG Applications
DeepLearning.AI
|
Short course | Intermediate | You already know basic RAG and need better retrieval, evaluation, and production-quality patterns. |
|
How to Evaluate AI Agents
Hugging Face Community
|
Evaluation guide | Intermediate | You need a practical evaluation plan built around representative tasks, controlled environments, observable traces, outcome and constraint metrics, repeated trials, and production failures turned into regression tests. |
|
Anthropic Model Hardware Standard preview
Anthropic
|
Agent interoperability standard | Intermediate to advanced | You want Anthropic's August 27, 2026 research preview of a model-agnostic standard for connecting agents to lab and manufacturing hardware through programmable interfaces and protocols such as MCP, with safety evaluation built into the rollout. |
|
Agent and Model Evaluations in Gemini Enterprise Agent Platform
Google AI for Developers
|
Evaluation guide | Intermediate to advanced | You want Google's practical July 31, 2026 guide to running the same agent and model evaluations during development and on production traffic, with metrics for quality, safety, grounding, tool use, and trajectories. |
|
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
Hugging Face
|
Training guide | Advanced | You need an end-to-end August 26, 2026 recipe for adapting a late-interaction retriever to your own domain, from datasets and losses through training, evaluation, and index optimization. |
|
Automating repetitive work at OpenAI with Codex
OpenAI
|
Practical guide | Intermediate | You want an August 25, 2026 field guide to capturing recurring engineering and evaluation work in reviewable notebooks, keeping approval boundaries explicit, and feeding lessons from earlier agent runs into the next run. |
|
How to Choose the Best AI Model (Live, in Your Editor)
OpenRouter
|
Model selection guide | Intermediate | You want a practical six-step workflow for shortlisting models from live data, testing them on your own prompts, measuring cost per completed task, and choosing or routing from inside a coding assistant. |
|
OpenAI eval design guide
OpenAI
|
Guide | Intermediate | You need practical guidance for designing representative eval datasets, choosing graders, and turning model testing into an engineering loop instead of ad hoc spot checks, especially while OpenAI's older Evals platform is winding down toward read-only status on October 31, 2026. |