MLflow is a platform for managing workflows and artifacts across the machine learning lifecycle, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. It has built in integrations with many popular ML libraries (TensorFlow, PyTorch, XGBoost, etc), but can be used with any library, algorithm, or deployment tool. MLflow’s components are: MLflow Tracking: An API for logging parameters, code versions, metrics, model environment dependencies, and model artifacts when running your machine learning code. MLflow Models: A model packaging format and suite of tools that let you easily deploy a trained model for batch or real time inference. MLflow Model Registry: A centralized model store, set of APIs, and UI focused on the approval, quality assurance, and deployment of an MLflow Model. MLflow Projects: A standard format for packaging reusable data science code that can be run with different parameters to train models, visualize data, or perform any other data science task.
Deploy MLflow on Kubernetes via Helm.
Log parameters, metrics, and artifacts across ML runs.
Version, stage, and manage model lifecycle centrally.
Standardize ML workflows with tracked runs and artifacts.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy MLflow on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by MLflow. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
