Domain-Specific AI Model Training Pipeline

General-purpose AI models are powerful but often miss domain-specific terminology, context, and nuance — a legal AI that confuses case law jurisdictions, or a medical AI that misuses clinical terminology. This workflow orchestrates a fine-tuning pipeline: your proprietary training data (conversation examples, document pairs, task demonstrations) is collected and formatted, validation sets are constructed, a foundation model (GPT-3.5-turbo-ft, Mistral, or Llama) is fine-tuned via the relevant API, the fine-tuned model is evaluated against a test set with automated metrics, and the passing model is deployed to a production endpoint. Training runs are tracked in MLflow or Weights & Biases. The pipeline handles data preparation, training, evaluation, and deployment automatically.

• Domain-specific terminology and context handled correctly
• Fine-tuned models can outperform prompt-engineered general models at specific tasks
• Automated pipeline removes engineering bottleneck from model iteration
• Evaluation metrics ensure only better models reach production
• MLflow tracking provides complete experiment history and reproducibility