#MLflow
5 related articles
From Spot Fixes to Measured Gains: Optimizing Document Extraction with ai_extract and MLflow
Document extraction optimization often gets stuck in a spot-fix loop without measurement or version control. This article shows how to combine ai_extract's declarative schema with MLflow Prompt Registry's version tracking to build a field-level evaluation loop, turning extraction accuracy from a subjective feeling into a quantifiable, reproducible engineering metric.
Stop Guessing with Your Agent: Building Measurable LLM Apps with DSPy + MLflow
Stop improving AI Agents by gut feeling. This workshop breakdown covers DSPy structured prompting, baseline evaluation, failure mode analysis, and MLflow experiment tracking.
From Demo to Production: Curing LLM Pipeline Drift with DSPy and MLflow
Learn how DSPy signatures and modules replace brittle hand-crafted prompts, and how MLflow tracking ensures reproducibility — systematically curing LLM pipeline drift from dev to production.
TutorialsMLflow Hands-On Tutorial: A Full-Stack MLOps Guide for GenAI Tracing + Classic ML Tuning
Learn MLflow's core features for GenAI and classic ML: auto tracing, model evaluation, Prompt versioning, hyperparameter tuning, and model deployment in just a few lines of code.
TutorialsThe Complete Guide to MLflow: An Open-Source AI Engineering Platform from Experiment Tracking to LLM Deployment
A deep dive into the MLflow open-source AI engineering platform, covering experiment tracking, LLM evaluation, model deployment, and monitoring to help teams efficiently manage the ML lifecycle.