Ensure You Have the Capabilities to Win with GenAI
Model Development & Training Foundations
Workshop
Build a repeatable training foundation—selection, tuning, and evaluation
Strong model outcomes come from disciplined development practices: clear lifecycle stages, fit-for-purpose algorithm choices, and rigorous evaluation. This workshop helps teams align on the ML lifecycle for GenAI.
Leave with a training foundation blueprint—process, tuning strategy, evaluation metrics, and quality practices.
The Challenge
Model development efforts often move quickly, but lack the shared standards needed to produce reliable, comparable results.
- The lifecycle isn’t consistent: Teams skip steps or use different stages and definitions, making results hard to compare.
- Tuning is inefficient: Hyperparameter strategies are unclear, leading to slow iteration and unpredictable gains.
- Evaluation lacks rigor: Models are assessed without consistent metrics, baselines, or explainability expectations.
Without common foundations, model training becomes inconsistent—slowing progress and weakening confidence in results.
Our Solution
We guide your team through a practical approach to establish the foundational practices required for disciplined model development and training.
- ML Lifecycle for GenAI: Define the end-to-end lifecycle stages and decision gates from problem framing through evaluation.
- Algorithm and Approach Selection: Establish selection criteria aligned to goals, constraints, and enterprise expectations for reliability and trust.
- Training Process and Workflow: Define the core training steps, roles, and handoffs that make development repeatable across teams.
- Tuning Strategy and Experiment Design: Establish a practical tuning approach with experimentation discipline to improve performance efficiently.
- Evaluation, Reproducibility, and Explainability Practices: Define evaluation procedures, key metrics, and the practices required to reproduce and explain results.
Area of Focus
- Understanding the Overall Machine Learning Lifecycle
- Choosing Appropriate Algorithms and Outlining the Training Process
- Planning Hyperparameter Tuning Strategies
- Establishing Rigorous Model Evaluation Procedures with Key Metrics
- Ensuring Model Reproducibility and Explainability
Participants Will
- Align on a shared ML lifecycle and decision gates for GenAI model development.
- Define criteria to select modeling approaches appropriate to business goals and constraints.
- Establish a repeatable training workflow teams can apply consistently.
- Create a tuning and experimentation plan to improve performance with less waste.
- Leave with evaluation metrics and reproducibility practices that increase trust in results.
Who Should Attend:
Solution Essentials
Format
Facilitated workshop (in-person or virtual)
Duration
4 hours
Skill Level
Intermediate
Tools
Slides, workshop templates, key worksheets, checklists, and collaboration tools.