MLflow helps teams build, track, and manage the full lifecycle of AI applications, from development to production.
MLflow offers tools for tracking experiments, managing models, and deploying AI workflows, including features like LLM tracing and prompt optimization. You can log metrics with `mlflow.log_metric()` or run projects using `mlflow run` to manage the entire AI application lifecycle, making it easier to manage models via the MLflow Model Registry.
MLflow helps teams build, track, and manage the full lifecycle of AI applications, from development to production.
Data scientists, ML engineers, and AI developers needing to manage the full lifecycle of their machine learning and AI models, from experimentation to production, will find MLflow useful.