How to Deploy Your Hugging Face Model to Production at Scale - MLOps Live #20, Oct, 25 at 12pm ET
Consume MLRun, Iguazio’s open source framework, to orchestrate ML pipelines from the research stage to production-ready AI applications. With a feature store and a modular strategy, MLRun enables a simple, continuous, and automated way of creating scalable production pipelines. MLRun automates the build process, execution, data movement, scaling, versioning, parameterization, outputs tracking, CI/CD integration, deployment to production, monitoring, and more.
Run multiple experiments in parallel, each using a different combination of algorithm functions and parameter sets (hyper-parameters) to automatically select the best result.
Describe and track code, metadata, inputs and outputs of machine learning related tasks (executions) and re-use results with a generic and easy-to-use mechanism.
Maintain the same set of features in the training and inferencing (real-time) stages with MLRun’s unified feature store.
Natively integrate with Kubeflow Pipelines to compose, deploy and manage end-to-end machine learning workflows with UI and a set of services.
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Learn how you can automate, scale and orchestrate pipelines end to end with the Iguazio MLOps Platform