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Streamline ML Workflows: Built for Data Scientists, Not Data Pipelines
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Beyond Orchestration: A Unified Platform for ML Workflows
Airflow excels at orchestrating workflows, but Outerbounds is purpose-built for machine learning. With integrated compute management, data versioning, and deployment tools, Outerbounds simplifies ML workflows from start to finish. Say goodbye to DAG files and manual configurations and hello to a Python-centric platform designed for data scientists.
ML-Specific Tools
Orchestration Tailored for ML
Outerbounds provides native support for experiment tracking, versioning, and deployment alongside orchestration, offering a seamless ML pipeline out of the box.
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Simplified Workflow Management
Python-First, No DAG Files
Define workflows in Python, not separate DAG files. Outerbounds eliminates Airflow's complexity, making orchestration more accessible for ML practitioners.
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Built-In Compute Management
From Local to Cloud, Effortlessly
Transition smoothly from local development to scalable cloud execution with Outerbounds' native compute management and scaling tools.
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Event-Driven and Cost-Effective
Automate and Optimize ML Workloads
Outerbounds supports event-driven workflows and provides built-in tools to track and optimize cloud spend, ensuring efficient and scalable ML pipelines.
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Start building today
Drop in any Friday at 9am PT for an open Q&A with our team. Whether you're curious about Outerbounds or have specific questions — nothing is off limits.
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