ClearML is an open-source MLOps and LLMOps platform designed to streamline the entire AI/ML workflow—from experiment tracking to model deployment. It addresses the common pain points of reproducibility, scalability, and collaboration in machine learning projects by providing integrated tools for experiment management, data versioning, pipeline orchestration, and model serving. Built for data scientists, ML engineers, and DevOps teams working with PyTorch, TensorFlow, Scikit-learn, and other frameworks, ClearML requires only two lines of code to start tracking experiments automatically. With support for Kubernetes, cloud providers (AWS, GCP, Azure), and on-prem infrastructure, it enables teams to scale their workflows without rewriting code or managing complex toolchains.
The platform combines a web-based UI with Python SDKs and CLI tools to create a unified environment for managing experiments, datasets, pipelines, and model deployments. Its modular architecture allows teams to adopt components incrementally—whether starting with experiment logging or deploying full CI/CD pipelines. ClearML’s auto-detection of frameworks, hyperparameters, and environment states eliminates manual logging overhead, making it ideal for teams looking to reduce boilerplate while maintaining full traceability and reproducibility.
What You Get
- Experiment Management - Automatically logs source code (including uncommitted changes), environment dependencies, hyperparameters (from argparse, Hydra, Click), model weights, metrics, and visualizations like TensorBoard plots, images, audio, and videos with just two lines of code.
- MLOps / LLMOps Orchestration - Automates execution of ML tasks across Kubernetes, cloud instances, or bare metal using ClearML Agent; supports autoscaling workers and queue-based task scheduling with built-in monitoring.
- Data Management - Version control for datasets stored on S3, Google Storage, Azure Blob, or NAS via clearml-data CLI; enables reproducible data pipelines with checksum-based change detection.
- Model Serving - Deploys models as scalable endpoints in under 5 minutes using NVIDIA Triton backend with built-in monitoring for latency, throughput, and model drift.
- Fractional GPUs - Allocates GPU memory at the driver level per container, enabling multiple experiments to share a single GPU without conflicts.
- Jupyter & PyCharm Integration - Seamless tracking of notebooks and remote debugging in PyCharm with full experiment context preserved.
- Pipeline Orchestration - Build and visualize multi-step pipelines from existing experiments; support for nested pipelines and dependency resolution.
- Hyperparameter Optimization - Automated Bayesian optimization over hyperparameters using Optuna integration, with results visualized in the web UI.
Common Use Cases
- Building a multi-tenant SaaS dashboard with real-time analytics - Teams use ClearML to track hundreds of concurrent experiments across different customer segments, version datasets per tenant, and deploy optimized models via Triton serving with monitoring.
- Creating a mobile-first e-commerce platform with 10k+ SKUs - ClearML’s data versioning and pipeline orchestration enable consistent feature engineering and model retraining across thousands of product categories with automated re-deployment triggers.
- Problem: Reproducibility failures due to untracked environments → Solution: ClearML auto-logs dependencies, code versions, and hyperparameters so any experiment can be re-run identically across machines.
- DevOps teams managing microservices across multiple cloud providers - ClearML Agent deploys ML workloads on AWS, GCP, and Azure with identical configuration; fractional GPU support reduces infrastructure costs while maintaining performance.
Under The Hood
ClearML is a Python-based MLOps platform designed to streamline machine learning experiment tracking, model management, and workflow automation. It provides a unified interface for managing ML projects across diverse compute environments, integrating seamlessly with popular frameworks and cloud storage providers.
Architecture
ClearML follows a layered architecture with well-defined modules for core MLOps functionalities such as task management, model versioning, and dataset handling. The system emphasizes separation of concerns with distinct layers for backend services, configuration, and user interaction.
- Modular organization supporting extensibility across ML frameworks and compute environments
- Clear separation between CLI tools, API services, and internal task execution logic
- Support for both local and distributed execution with consistent abstraction layers
Tech Stack
Built entirely in Python, ClearML leverages a wide range of standard libraries and cloud integration points to support diverse ML workflows.
- Python as the primary language with multi-version compatibility and extensive framework support
- Integration with AWS S3, Google Cloud Storage, and Azure Blob Storage for data management
- Utilizes setuptools for packaging and configuration scripts to ensure cross-environment deployment
- Comprehensive linting and testing practices including flake8 checks and API-level test coverage
Code Quality
The codebase reflects a mature Python project with consistent structure and error handling practices, though some areas show signs of technical debt.
- Extensive use of error handling and logging to ensure robustness across distributed environments
- Consistent naming conventions and modular organization facilitate maintainability
- Type annotations and linting configurations support code quality standards
- Test coverage is comprehensive for core modules but limited in peripheral components
What Makes It Unique
ClearML stands out in the MLOps space through its intelligent automation features and seamless integration with existing ML workflows.
- Unified abstraction layer that simplifies transitions between local, cloud, and distributed training environments
- Dynamic resource scaling and intelligent job scheduling for efficient ML workload management
- Multi-optimizer support in its hyperparameter tuning module, enabling flexibility across tuning strategies
- Interactive configuration wizard that streamlines credential setup in notebook and colab environments