Reviews, Scores & Student Outcomes
Summary
This summary is generated from verified AllPros reviews and may not include every detail.Created with AI, based on 5 verified reviews
Alexey has a strong, verifiable teaching track record through DataTalks. Club and the Zoomcamp series, and this course's curriculum is technically substantive covering RAG, agents, MCP, evals, and monitoring with real tooling.
It demands real coding effort and prerequisites (Python, Docker, Git), so it suits builders, not casual learners. At $1,799, it's a serious investment, value depends on how much you complete and apply.
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Strong verified signals for AI Engineering Buildcamp: From RAG to Agents.
Score history
Cumulative verified score over the last 6 months
5.0/5
2026 average·5 reviews
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Program traits
What verified reviewers flagged most often
5 students tagged at least one strength
- Practical content80%TOP
- Real experience80%TOP
- Fast results60%AVG
- High availability40%AVG
- Clear explanations20%LOW
- Community support20%LOW
AI Engineering Buildcamp: From RAG to Agents worth the money?
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100% of verified reviewers said yes.
How many students finished the course?
Share of verified reviewers who completed the full program. Green = finished, red = did not finish.
- Finished100%
- Did not finish0%
About the creator
Alexey Grigorev
View full profileMachine Learning · Berlin, Germany
AI Engineering Buildcamp: From RAG to Agents
1 program on AllPros
5
Reviews
Alexey Grigorev is the founder of DataTalks.Club and creator of the Zoomcamp series, with 15 years of experience teaching AI and data to 100k+ students. He started his career as a Java developer before moving into data science, later working as a Principal Data Scientist at OLX Group. He's also a Kaggle Master and published author.
Ratings & reviews
Ratings & reviews
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Program Overview
Learning format
Subcategory
Course language
Official program page
Price
Price may change · updated within 1–2 weeks
Learning format
Subcategory
Course language
Official program page
Price
Price may change · updated within 1–2 weeks
What You'll Learn
- Set up monitoring with Grafana, Logfire, and OpenTelemetry
- Test and evaluate AI outputs using LLM-as-judge methods
- Expose and extend agent tools via MCP
- Use PydanticAI and OpenAI's Agent SDK for agents
- Add agentic behavior using function calling and tool use
- Build a working RAG-based AI assistant from scratch
Best For
- Data scientists ready to add AI skills
- Software engineers curious about building LLMs
- ML engineers wanting production AI experience
- Coders stuck at the tutorial phase
Not For
- Complete beginners with no coding background
- Non-technical marketers or business roles
- Those unwilling to commit weekly hours
- People wanting theory without hands-on building
What's included
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