Teaching ML Like Production

This entry describes educational work at TripleTen, where I started in May 2024 as a Data Science and AI/ML Instructor. The title expresses an evaluation-minded teaching approach; it does not claim responsibility for deploying learners’ systems. Current employment status is not asserted.

Responsibilities

My work included reviewing data-science projects, delivering technical feedback, supporting learners through live sessions and debugging, and teaching practical Python and machine-learning workflows.

The emphasis was making modeling choices and evaluation assumptions discussable: what the task requires, how the data is prepared, what a validation strategy can establish, and how to explain a result clearly.

A useful review lens

• Clarify the prediction or decision the project is meant to support.
• Examine data assumptions and potential leakage.
• Ask whether validation and metrics reflect the task.
• Discuss model tradeoffs alongside implementation complexity.
• Make feedback specific enough to guide debugging and revision.

These are review principles, not a claim that a formal framework was adopted across the organization.

Evidence boundaries

This public retrospective preserves the role and responsibilities described in the professional record. It does not publish private learner submissions, internal rubrics, measured learning outcomes, or a claim of production deployment. No broader RAG/MLOps assessment remit or senior-candidate evaluation is inferred.

Reflection

Technical mentorship connects scientific reasoning with practical communication. Explaining a modeling decision requires making assumptions visible, distinguishing evidence from confidence, and giving feedback that can be acted on.