Fine-tuning open-source LLMs can unlock significant gains—but only when teams understand the trade-offs, data requirements, and long-term maintenance implications. This advanced workshop focuses on making fine-tuning a deliberate, measurable decision rather than an experimental gamble.
To win, teams must fine-tune open-source LLMs with clear intent, disciplined data pipelines, and measurable performance outcomes.
Teams pursuing LLM fine-tuning often run into:
- Unclear tuning choices: Apply fine-tuning techniques without understanding their trade-offs or when they are appropriate.
- Weak data pipelines: Struggle to prepare, manage, and reuse training data effectively.
- Unmeasured outcomes: Fine-tune models without clearly measuring performance gains or monitoring long-term drift.
Poor fine-tuning practices lead to wasted effort, unstable models, and unclear ROI.
In this hands-on workshop, your team designs and evaluates a disciplined fine-tuning approach for open-source LLMs, grounded in measurable outcomes.
- Examine fine-tuning methods and their practical trade-offs.
- Prepare and manage data pipelines to support reliable tuning.
- Select appropriate base models and transfer approaches.
- Monitor tuned models for drift and determine when retraining is needed.
- Evaluate performance gains to assess the impact of fine-tuning.
- Understanding Fine-Tuning Methods and Trade-offs
- Preparing Data Pipelines for Tuning
- Selecting Base Models and Transfer Approaches
- Monitoring Model Drift and Re-Training Needs
- Evaluating Performance Gains Post-Tuning
- Understand when fine-tuning open-source LLMs is the right approach.
- Choose fine-tuning methods aligned to specific goals and constraints.
- Build data pipelines that support repeatable tuning efforts.
- Monitor tuned models for degradation and retraining triggers.
- Measure and communicate the real performance impact of fine-tuning.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Advanced; prior experience with LLMs and model training recommended
Open-source LLMs, fine-tuning workflows, and evaluation artifacts