A deeper, hands-on extension of the introductory GenAI workshop. Move from general examples to organisation-specific workflows – optionally using your own datasets and real business problems.
DURATION
2 full day: 14 hrs instruction + exercises
FORMAT
Online workshop: Clean workspace per participant
GROUP SIZE
6 – 12 Recommended participants
PREREQUISITES
Python & CLI + Data science experience
This two-day program builds on core GenAI implementation patterns and evaluation concepts, while introducing client-specific exercises and practical agentic development approaches applied to your team’s own datasets and business problems.
Full GenAI foundation included
Everything from the 1-day course - field updates, LLM patterns, evaluation basics, and agent coding - forms the foundation for day two.
Tailored exercises on your data
Day two introduces exercises built specifically around the client's own datasets, making outcomes directly relevant to daily work.
Agentic development with Claude Code
Build agentic workflows directly targeting your team's concrete business problems - with reliability and evaluation built in from the start.
Emphasis on reliability
Evaluation and quality thinking are woven throughout - so workflows are designed to hold up in production, not just in demos.
Seven core topics across both days – building from the GenAI foundation into applied, organisation-specific implementation.
This workshop is ideal for teams with Python and data science experience who want to move beyond isolated LLM experiments and apply GenAI to real, organisation-specific workflows.
Data scientists
Teams working with ML models who want to integrate GenAI into existing workflows responsibly - and apply it to their own datasets and daily problems.
ML engineers
Engineers ready to move beyond single LLM calls and evaluate how agentic patterns and GenAI workflows integrate into their production systems.
Technical leads
Team leads who want their team to develop applied, organisation-specific GenAI capability - not just general familiarity with the technology.
Participants will move from experimenting with individual LLM calls to designing and implementing structured data science workflows on their own business problems. They’ll understand agentic development approaches, integration patterns, evaluation methods, and how to reason about reliability and cost in production contexts.
The 1-day workshop is a structured entry point covering GenAI fundamentals for data science teams. The 2-day format includes everything from day one, then adds tailored exercises on the client’s own datasets and deeper agentic development work focused on the team’s actual business problems — making outcomes directly actionable.
Yes, this is the core design principle of the 2-day format. A discovery phase before training aligns the program with your team’s technical maturity, tooling, datasets, and goals. Day two exercises are built specifically around your organisation’s context. For even deeper customisation including RAG pipelines on company data, the 3-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 for the 2-day format – the first day covers all foundational concepts before moving to applied work on day two.
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 team to build with GenAI – on your own problems.