Why Metaflow?
9 months ago
- #Metaflow
- #Machine Learning
- #Data Science
- Modern businesses are eager to utilize data science and ML, moving away from custom systems.
- DS/ML applications require a common foundation for quicker and more robust development.
- All DS/ML applications use data, needing easy access and processing regardless of source.
- DS/ML applications perform computation, requiring reliable and scalable cloud resources.
- DS/ML applications consist of interconnected parts, needing workflow orchestration for execution.
- DS/ML applications evolve incrementally, requiring tracking, organization, and versioning.
- DS/ML applications produce business value by integrating with surrounding systems.
- DS/ML applications should leverage the best tools, including off-the-shelf libraries or custom approaches.
- Metaflow covers the full stack of DS/ML infrastructure, aiding quick iteration and deployment.
- Metaflow handles low-level infrastructure, allowing focus on application and model development.
- Metaflow relies on proven, scalable infrastructure, integrating with top clouds and Kubernetes.
- Metaflow is used by hundreds of companies, with commercial support from Outerbounds.