Course Outline

Introduction to Fine-Tuning Challenges

  • Overview of the fine-tuning process for government applications
  • Common challenges encountered when fine-tuning large models for government use
  • Understanding the impact of data quality and preprocessing on model performance for government tasks

Addressing Data Imbalances in Government Datasets

  • Identifying and analyzing data imbalances in public sector datasets
  • Techniques for handling imbalanced datasets in government projects
  • Using data augmentation and synthetic data to improve model fairness for government applications

Managing Overfitting and Underfitting in Government Models

  • Understanding overfitting and underfitting in the context of government models
  • Regularization techniques: L1, L2, and dropout for enhancing model robustness in public sector tasks
  • Adjusting model complexity and training duration to optimize performance for government operations

Improving Model Convergence for Government Applications

  • Diagnosing convergence problems in government models
  • Choosing the right learning rate and optimizer for public sector tasks
  • Implementing learning rate schedules and warm-ups to enhance model training efficiency for government use

Debugging Fine-Tuning Pipelines for Government Projects

  • Tools for monitoring training processes in government environments
  • Logging and visualizing model metrics for improved transparency and accountability in public sector operations
  • Debugging and resolving runtime errors to ensure reliable performance for government tasks

Optimizing Training Efficiency for Government Models

  • Batch size and gradient accumulation strategies for enhancing training efficiency in government projects
  • Utilizing mixed precision training to improve computational efficiency for government applications
  • Distributed training for large-scale models to support high-performance computing needs in the public sector

Real-World Troubleshooting Case Studies for Government Applications

  • Case study: Fine-tuning for sentiment analysis in government communications
  • Case study: Resolving convergence issues in image classification for government surveillance systems
  • Case study: Addressing overfitting in text summarization for government reports and documents

Summary and Next Steps for Government Projects

Requirements

  • Experience with deep learning frameworks such as PyTorch or TensorFlow for government applications
  • Understanding of machine learning concepts, including training, validation, and evaluation processes
  • Familiarity with fine-tuning pre-trained models to meet specific project requirements

Audience

  • Data scientists
  • AI engineers
 14 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

Troubleshooting Fine-Tuning Challenges

2026-02-25 09:30
14 hours
Cleveland, OH
$ 2703 (Online)
$ 3903 (Classroom)

Troubleshooting Fine-Tuning Challenges

2026-03-11 09:30
14 hours
Cleveland, OH – Regus Cleveland City Center
$ 2703 (Online)
$ 3903 (Classroom)

Troubleshooting Fine-Tuning Challenges

2026-03-25 09:30
14 hours
Columbus, OH - Galleria at PNC Plaza
$ 2703 (Online)
$ 3903 (Classroom)

Troubleshooting Fine-Tuning Challenges

2026-04-08 09:30
14 hours
Ann Arbor, MI – Regus - South State Commons I
$ 2703 (Online)
$ 3903 (Classroom)

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