- Stars
- 10,038
- License
- LGPL-3.0
- Last commit
- 4 months ago
Jupyter NotebookStable
Best AutoML & ML Workflow Tools Tools
Explore leading tools in the AutoML Tools category, including open-source options and SaaS products. Compare features, use cases, and find the best fit for your workflow.
7 open-source projects · 3 SaaS products
Top open-source AutoML Tools
These projects are active, self-hostable choices for knowledge management teams evaluating alternatives to SaaS tools.
- Stars
- 9,814
- License
- Apache-2.0
- Last commit
- 6 days ago
PythonActive
- Stars
- 9,290
- License
- Apache-2.0
- Last commit
- 1 month ago
PythonActive
- Stars
- 8,039
- License
- BSD-3-Clause
- Last commit
- 1 day ago
PythonActive
- Stars
- 4,280
- License
- MIT
- Last commit
- 1 day ago
Jupyter NotebookActive
- Stars
- 3,234
- License
- MIT
- Last commit
- 6 months ago
PythonStable
Recently updated
1 day ago
FLAML delivers rapid, cost-effective AutoML and hyperparameter optimization for classification, regression, and LLM workflows, supporting Python 3.9+, MLflow integration, and a multi-agent AutoGen framework.
Popular SaaS Platforms to Replace
Understand the commercial incumbents teams migrate from and how many open-source alternatives exist for each product.
Azure Machine Learning
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
AutoML Tools
Alternatives tracked
7 alternatives
H2O Driverless AI
Automated machine learning platform for building AI models without coding
AutoML Tools
Alternatives tracked
7 alternatives
Vertex AI
Unified ML platform for training, tuning, and deploying models
AutoML Tools
Alternatives tracked
7 alternatives
Most compared product
7 open-source alternatives
Azure Machine Learning is a cloud service that accelerates and manages the machine learning (ML) project lifecycle. It provides an environment for data scientists to develop models (using tools like notebooks or AutoML), manage experiments, and deploy models as RESTful endpoints. The service also supports MLOps with features for tracking experiments, model versioning, and integration with CI/CD, enabling teams to build, train, and deploy AI models at scale while maintaining governance and reproducibility.
Leading hosted platforms
Frequently replaced when teams want private deployments and lower TCO.
Explore related categories
Browse neighbouring categories in ML & AI to widen your evaluation.
- AI Agent FrameworksFrameworks for building and orchestrating agentic LLM applications: tool use, multi-agent workflows, memory, planning, and evaluation.
- AI App BuildersPlatforms that generate production web apps from natural-language prompts, producing runnable code (repo/PR/deploy) with iterative editing.
- AI Application Frameworks & OrchestrationBuild AI apps/agents with tools, retrieval, prompts and routing graphs.
- Conversational AI PlatformsPlatforms to design, build, and deploy chat/voice assistants with NLU/dialogue, channels, policies, analytics, and hosting.
- Data Labeling & AnnotationAnnotate images, text and audio with workflows, QA and datasets.
- Feature StoresManage, compute and serve shared ML features across offline/online flows.