Creating ML apps should be simple and frictionless.
UnionML is an open source Python framework built on top of Flyte, unifying the complex ecosystem of ML tools into a single interface.
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Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them.
Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning.
Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place.
Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior.
This helps you maintain consistent code across your ML stack, from training to prediction logic.
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Begin with a proof-of-concept, starting with small data or simple models to make sure the ML system as a whole works.
Your laptop – or remote jupyter environment – acts as a regularizer so you can scale your dataset set and reach for more or less complex models depending on where you are in the prototyping phase.
Once you’re confident your ML system behaves the way you intend it to, leverage the power of Flyte and UnionCloud to scale up your training workload in a vendor-agnostic way!
You shouldn’t need to bug an MLOps engineer to accelerate your workloads... just request them through the function decorators!
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No need to re-implement this logic in multiple places based on your use case.
Implement feature transform and prediction logic once, deploy to production in batch and online serving contexts.
Streaming coming soon!
With built-in data lineage and registry capabilities, never worry about losing experiment metadata again, even when performing ad-hoc experiments!
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We’re building the next generation of web-native machine learning tools so that building ML apps is as easy as building web apps. If you want to join us in our mission:
