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Dotscience, a provider of DevOps for Machine Learning (MLOps) solutions, is forming partnerships with GitLab and Grafana Labs, along with strengthening integrations with several platforms and cloud providers.
The company is deepening integrations to include Scikit-learn, H2O.ai and TensorFlow; expanding multi-cloud support with Amazon Web Services (AWS) and Microsoft Azure; and a entering a joint collaboration with global enterprises to develop an industry benchmark for helping enterprises get maximum ROI out of their AI initiatives.
“MLOps is poised to dominate the enterprise AI conversation in 2020, as it will directly address the challenges enterprises face when looking to create business value with AI,” said Luke Marsden, CEO and founder at Dotscience. “Through new partnerships, expanded multi-cloud support, and collaborations with MLOps pioneers at global organizations in the Fortune 500, we are setting the bar for MLOps best practices for building production ML pipelines today.”
Grafana Labs, the open observability platform, and Dotscience are partnering to deliver observability for ML in production.
With Dotscience, ML teams can statistically monitor the behavior of ML models in production on unlabelled production data by analyzing the statistical distribution of predictions.
The partnership simplifies the deployment of ML models to Kubernetes and adds the ability to set up monitoring dashboards for deployed ML models using cloud-native tools including Grafana and Prometheus, which reduces the time spent on these tasks from weeks to seconds.
As a GitLab Technology Partner, Dotscience is extending the use of its platform for collaborative, end-to-end ML data and model management to the more than 100,000 organizations and developers actively using GitLab as their DevOps platform.
Dotscience is now available on the AWS Marketplace, enabling AWS customers to easily and quickly deploy Dotscience directly through AWS Marketplace’s 1-Click Deployment, and through Microsoft Azure.
Dotscience has expanded the frameworks in which data scientists can deploy tested and trained ML models into production and statistically monitor the productionized models, to include Scikit-learn, H2O.ai and TensorFlow.
These new integrations make Dotscience’s recently added deploy and monitor platform advancements—the easiest way to deploy and monitor ML models on Kubernetes clusters—available to data scientists using a greater range of ML frameworks.
