At Seldon, we are committed to our open-core strategy and continue to create impactful technology for our open-source community. There are several barriers to not only deploying models and having the ability to do this in a scalable manner. It can be a struggle to build advanced ML applications and advanced data flows, which can prevent a team from using its valuable time and resources in an efficient way. That’s why we are thrilled to announce our latest update of our open-source framework, Seldon Core V2, which is enabling DevOps, ML Engineers, and data scientists to quickly and easily deploy models and experiments at scale.  We are proud to have developed Seldon Core V2 in collaboration with [ML@CL] lab from the University of Cambridge.

In this session, the Seldon technical dive into the details of the release and demonstrate the cutting-edge features, highlights include:

• A data-centric approach to model inference with ML-focused Pipelines

• Multi-model serving with overcommit

• New resource types: Models, Servers, Pipelines, Experiments

• Local Docker and Kubernetes installations

• Simple Kubernetes integration

• Wide machine learning artifact support

Watch the launch in full below: