A structured, hands-on introduction to Generative AI for data science teams. Move from single LLM calls to designing and implementing production-grade AI workflows in a single day.
This one-day workshop focuses on implementation patterns, technical constraints, and realistic system behaviour – designed as a low-friction entry point for data science teams evaluating GenAI adoption.
Generative AI field update
Understand what is new and relevant in the GenAI landscape - grounded in practical engineering, not hype. Focus on what actually matters for data science workflows in 2026.
Coding with AI agents
Hands-on introduction to building with AI Agents. Each participant works in a clean, isolated workspace to ensure safe and reproducible learning outcomes.
LLM integration patterns
Practical patterns for integrating Large Language Models into data science applications. Covers common mistakes, cost implications, and reliability considerations.
Evaluation & quality basics
Learn how to assess and measure LLM output quality. Build risk awareness and understand reliability constraints before committing to larger architectural investments.
DURATION
1 full day: 7 hrs instruction + exercises
FORMAT
Online workshop: Clean workspace per participant
GROUP SIZE
6 – 12 Recommended participants
PREREQUISITES
Python & CLI + data science experience
Six core topics covered across the day, building from fundamentals to practical implementation.
This workshop is ideal for teams with Python and data science experience who want to move beyond isolated LLM experiments toward integrated workflows.
Data scientists
Teams working with ML models who want to integrate GenAI into their existing workflows responsibly.
ML engineers
Engineers evaluating LLM integration patterns before committing to larger architectural or strategic decisions.
Technical leads
Team leads assessing GenAI adoption readiness and wanting a structured, low-risk pilot before broader commitment.
Participants will move from experimenting with individual LLM calls to designing and implementing structured data science workflows. They’ll understand integration patterns, evaluation approaches, and how to reason about reliability and cost in production contexts.
Yes. The 1-day format includes a discovery phase where we align the program with your team’s technical maturity, tooling, and goals. For deeper customisation including exercises on your own datasets, the 2-day format is recommended.
Participants should be comfortable with Python, the command line interface, and have prior data science experience. No prior GenAI or LLM experience is required — this workshop is designed as an entry point for teams new to the space.
TSW Academy’s programs combine engineering discipline with architectural thinking. We focus on realistic system behaviour, common failure modes, and production readiness — not just how to make impressive demos. Our instructors are practitioners, not generalist trainers.
You may cancel or reschedule free of charge up to three weeks before the scheduled date. Any preparation work already completed may be charged in the case of a late cancellation. The proposal is valid for 90 calendar days from issue.
Equip your data scientists to build with GenAI responsibly, at production scale.