MLOps Platforms
Machine Learning Operations (MLOps) platforms are the infrastructure layer of modern AI. They help teams efficiently move from experimentation to deployment, tracking models, datasets, and experiments in a reproducible, version-controlled environment.
These open-source tools streamline the ML lifecycle, from data preparation and pipeline orchestration to model monitoring and deployment. These frameworks make scaling and managing ML workflows far more sustainable.
| Platform | Description | Key Features | Link |
|---|---|---|---|
| MLflow | End-to-end lifecycle manager that tracks experiments and manages deployment pipelines. | Interactive UI for visualisation; integrates with any ML framework. | mlflow.org |
| ZenML | Pipeline orchestrator designed for reproducible and scalable ML workflows. | Metadata tracking; supports multiple clouds and experiment stores. | zenml.io |
| Kubeflow | Kubernetes-native platform for building and deploying ML systems at scale. | Jupyter Notebooks; AutoML tools; local setup via Minikube. | kubeflow.org |
| DVC (Data Version Control) | Git-style system for versioning datasets, models, and experiments. | Reproducible builds; seamless integration with S3, GCS, and other storage. | dvc.org |
| ClearML | Full-stack open-source suite for experiment management and orchestration. | Agent-based scheduling; free open-core model; web dashboard. | clear.ml |
| Weights & Biases (W&B) Open Source | Lightweight platform for experiment tracking and hyperparameter optimisation. | Dataset artefacts; sweeps for optimisation; collaborative dashboard. | wandb.ai/site/open-source |