Dotscience Offers Platform for Monitoring Machine Learning on Kubernetes

Dotscience is offering new platform advancements that make deploying and monitoring machine learning models on Kubernetes clusters simple and accessible to data scientists.

New Dotscience Deploy and Monitor features dramatically simplify the act of deploying ML models to Kubernetes and setting up monitoring dashboards for the deployed models with cloud-native tools Prometheus and Grafana.

Dotscience now also enables hybrid and multi-cloud scenarios where, for example, model training can happen on-prem using an attached Dotscience runner, and models can then be easily deployed to a Kubernetes cluster in the cloud for inference using a Dotscience Kubernetes deployer.

Dotscience also announced a joint effort with S&P Global to develop best practices for collaborative, end-to-end ML data and model management that ensure the delivery of business value from AI.

Dotscience enables data science and ML teams to own and control the entire model development and operations process, from data ingestion, through training and testing, to deploying straight into a Kubernetes cluster, and monitoring that model in production to understand its behavior as new data flows in.

Data science and ML teams can use Dotscience to ingest data, perform data engineering, train and test models and then deploy them to CI for further testing before final deployment to production with a single click, command or API call where the models can then be statistically monitored.

Dotscience’s Deploy gives users the ability to:

  • Handle both building the ML model into a Docker image and deploying it to a Kubernetes cluster
  • Hand the entire CI/CD responsibility over to existing infrastructure, if preferred, or use lightweight built-ins
  • Track the deployment of the ML model back to the provenance of the model and the data it was trained on to maintain accountability across the entire ML lifecycle

Users can deploy their models in three main ways:

  • UI deployments - After defining parameters in the UI, users can deploy straight from within the Dotscience Hub interface
  • CLI style - The Dotscience CLI tool ‘ds’ can be used to deploy an ML model using command line parameters to define the exact details
  • From the Python library - deploy directly from the python library with ds.publish(deploy=True), which also automatically sets up a statistical monitoring dashboard

Dotscience’s statistical monitoring feature allows ML teams to define which metrics they would like to monitor on their deployed models and then bring those metrics straight back into the Dotscience Hub interface where the team first developed the model.

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