The open-source MLOps framework speeds the creation and deployment of ML microservices within a unified interface
Union.ai recently announced the release of UnionML. The open-source MLOps framework for building web-native machine learning applications offers a unified interface for bundling Python functions into machine learning (ML) microservices. It is the only library that seamlessly manages data science workflows and production lifecycle tasks. This makes it easy to build new AI applications from scratch or make existing Python code run faster at scale.
UnionML aims to unify the ever-evolving ecosystem of machine learning and data tools into a single interface expressing microservices as Python functions. Data scientists can create UnionML applications by defining a few core methods automatically bundled into ML microservices, starting with model training and offline/online prediction.
“Creating machine learning applications should be easy, frictionless and simple, but today it isn’t. The cost and complexity of choosing tools, deciding how to combine them into a coherent ML stack, and maintaining them in production require a team of people who often leverage different programming languages and follow disparate practices. UnionML significantly simplifies creating and deploying machine learning applications,” said Ketan Umare, CEO, Union.ai.
UnionML apps comprise two objects: Dataset and Model. Together, they expose function decorator entry points that serve as building blocks for a machine learning application. By focusing on the core building blocks instead of how they fit together, data scientists can reduce their cognitive load for iterating on models and deploying them to production.
UnionML uses Flyte to execute training and prediction workflows locally or on production-grade Kubernetes clusters, relieving MLOps engineers of the overhead provisioning compute resources for their stakeholders. Models and ML applications can be served via FastAPI or AWS Lambda. More options will be available in the future.