Workshop / 02
AI / ML
Platform
A structured path from experimentation to production ML engineering. Taught as a coherent sequence — not a scatter of tutorials.
Formats: foundations cohort · advanced practitioner · custom
Curriculum
- 01
ML Fundamentals
Supervised vs. unsupervised learning, model types, and the mental models every engineer needs.
- 02
Feature Pipelines
Data ingestion, transformation, and feature stores. Reproducible inputs that don't rot at 3am.
- 03
Model Evaluation
AUC, calibration, fairness, and evaluation harnesses that catch regressions before they hit production.
- 04
LLM Integration
Prompt engineering, RAG, tool use, and grounding LLMs in your production data.
- 05
MLOps & CI/CD for Models
Versioning, experiment tracking, model registries, canary releases, and rollback strategies.
- 06
AI Governance & Safety
Model cards, release gates, bias audits, and the compliance artifacts your second line actually needs.
Delivery Formats
- Foundations Cohort3 days
Core concepts through hands-on labs. Ideal for teams new to production ML.
- Advanced Practitioner5 days
Deep-dive MLOps, LLM integration, and a live capstone project on your stack.
- Custom EngagementScoped
Shaped to your existing platform, data infrastructure, and business objectives.
Ready to run this with your team?
Schedule a Scoping Call