Explainable MLOps

Apply DevOps principles to Explainable ML systems (Explainable MLOps)

MLOps Features and Benefits

Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.

ML pipeline automation

Perform continuous training of the model by automating the ML pipeline; this lets you achieve continuous delivery of model prediction service. To automate the process of using new data to retrain models in production, you need to introduce automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management.

CI/CD pipeline automation

For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.

MLOps setup

MLOps setup includes the following components: Source control, Test and build services, Deployment services, Model registry, Feature store, ML metadata store, ML pipeline orchestrator.

Coming Soon

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