Hyperskill AI Engineering Workshops

Teams often spend weeks and money fine-tuning a model, then find out a better prompt or the right documents would have done the job.

In this free 75-minute live session, Nikalina Ogorodova, an AI/ML engineer at Hyperskill who has trained over 300 engineers, covers:

1. How LLMs are trained and where fine-tuning fits.
2. What to try first: better prompts, examples, or access to the right documents.
3. How to decide if fine-tuning pays off: the data you need, the cost, and how to check it helped.

The session runs on Zoom through Maven. To get the join link and the recording, register here: https://maven.com/p/c99d48/fine-tuning-intro-for-practitioners-and-the-curious

Want a head start? This short video covers when fine-tuning pays off and when prompting or RAG is enough: https://youtu.be/967KtpjBczI

Coding agents can read repos, run commands and change files. That makes them useful, and risky.
In this free live session, Nikalina Ogorodova, an AI/ML engineer at Hyperskill who has trained over 300 engineers, covers:

1. Inside the agent loop: one task traced through tool choice, context retrieval, and a codebase too large for the context window.
2. Where the risks come from: secrets in env or config files can end up in context, and hidden text in a file or web page can steer the agent.
3. Safer use and its limits: sandboxes reduce risk but do not guarantee isolation. You will practice limiting access, using a throwaway VM or container, and reviewing commands.

Participants get an API key to try the examples. The session runs on Zoom through Maven. To get the join link and the recording, register here: https://maven.com/p/20c602/coding-agents-architecture-and-security-intro