ClearML

ClearML is an open source Machine Learning Operations (MLOps) and Machine Learning (ML) orchestration platform that provides experiment tracking, data and model management, and workflow automation for ML projects in on-premises (on-prem), cloud, and hybrid environments.

• Open source MLOps platform for experiment tracking, orchestration, and workflow automation.
• Tools for managing datasets, models, and artifacts across the ML lifecycle.
• Execution, scheduling, and scaling of ML workloads on Kubernetes and other compute backends.
• Support for self-hosted and managed deployments for enterprises and teams.
• Integration with common ML frameworks and DevOps tooling.

More About ClearML

ClearML is positioned as an MLOps and ML orchestration platform (MLOps) designed to support teams building, training, and deploying ML systems across on-prem, cloud, and hybrid infrastructure. It focuses on connecting experimentation, data and model management, and production workflows into a single operational environment. Organizations use ClearML to log and compare experiments, organize datasets and models, and automate training and inference pipelines while maintaining traceability across the ML lifecycle.

The platform provides experiment tracking and workspace capabilities (ML experiment management), where users can capture code, parameters, metrics, logs, and artifacts from training runs. This tracking layer allows practitioners to reproduce runs, compare performance across experiments, and collaborate on shared projects. The same experiment metadata can be linked to datasets and models, which supports governance and auditability in enterprise environments.

ClearML includes pipeline and task orchestration components (workflow orchestration) that enable users to define, schedule, and automate ML workflows. These workflows can include data preparation, training, evaluation, and deployment steps and can be executed across heterogeneous compute resources. The system integrates with Kubernetes (container orchestration) and other execution backends so that jobs can be queued, scheduled, and scaled according to available resources and organizational policies.

Dataset and model management features (model and data management) provide versioning and lineage for ML assets. Teams can register models, associate them with specific experiments and datasets, and track which versions are used in production workflows. This supports practices such as model promotion between environments and rollback to previous versions when needed. Artifact storage can be configured to use various backends, depending on enterprise storage requirements.

ClearML exposes APIs and SDKs (developer tooling) for integration into Python-based ML workflows and Continuous Integration and Continuous Deployment (CI/CD) systems. It is built to work with common ML frameworks and libraries, which allows teams to adopt it without fully restructuring existing codebases. The platform can be deployed as a self-hosted solution in private VPCs or data centers, or consumed as a managed Software-as-a-Service (SaaS) offering, which aligns with varied security and compliance postures in enterprise environments.

In marketplace and directory taxonomies, ClearML fits into categories such as MLOps platforms, ML experiment tracking, ML workflow orchestration, and model and data management. Its open source core and enterprise-ready deployment options position it for organizations that need consistent tracking, orchestration, and governance across multiple ML projects and infrastructure types.