-   Name: SmallCon
-   Date: December 11, 2024
-   Focus: Strategies for updating machine learning models in production
-   Format: In-person


# 👥 Session Details

-   Time: 16:53
-   Type: Technical Presentation
-   Speaker: Arnav Garg, ML Engineering Lead, Predibase
-   Session Goal: Discuss strategies for updating machine learning models in production using data collected from production.


# 💡 Key Technical Insights

Training Strategies:

-   Continuous model quality improvement for production LLMs
-   Incremental fine-tuning for cost-effective updates
-   Rehearsal learning for performance enhancement
-   Hybrid approach combining:
    -   Incremental updates
    -   Periodic full retraining
    -   Performance/cost balance


# 🤖 Technical Implementation

Predibase Platform:

-   SDK and UI components
-   100+ base models for LoRA fine-tuning
-   Incremental training via `continue_from_version`
-   Configurable retraining interface

Deployment Options:

-   SDK integration
-   UI-based configuration
-   LoRA parameter customization
-   Learning configuration flexibility


# 📈 Performance Benefits

Efficiency Gains:

-   Improved precision and accuracy
-   Reduced training costs
-   Faster update cycles
-   Better data utilization

Production Advantages:

-   Continuous model improvement
-   Cost-effective updates
-   Rapid knowledge incorporation
-   User feedback integration


# 📋 Best Practices

Implementation Strategy:

1.  Start with Predibase platform exploration
2.  Experiment with incremental training
3.  Implement rehearsal learning
4.  Develop hybrid training approach
5.  Monitor performance metrics
6.  Optimize cost efficiency

Resources:

-   Predibase SDK documentation
-   Platform guidelines
-   Integration examples
-   Training configurations

The session highlighted how continuous model updates can be practically implemented in production environments, with particular emphasis on balancing performance improvements with operational costs through incremental training approaches.