LONDON & SAN FRANCISCO--(BUSINESS WIRE)--Delivering on its vision that ML engineering should be just as easy, fast and safe as modern software engineering when using DevOps techniques, Dotscience, the market leader in DevOps for Machine Learning (MLOps), today announced new partnerships with GitLab and Grafana Labs; deep integrations to include Scikit-learn, H2O.ai and TensorFlow; expanded multi-cloud support with Amazon Web Services (AWS) and Microsoft Azure; and 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.
Developing MLOps Gold Standard with Global Enterprises
AI-derived business value is forecast to reach $3.9 trillion in 2022. However, many businesses continue to struggle with deploying ML models into production, posing challenges around the value of AI in the enterprise. To address this challenge, Dotscience is collaborating with global enterprises to help them get the most from an early investment in AI.
"Dotscience not only gives productivity benefits to data scientists but also gives those in governance roles assurance that the firm is doing all it can to mitigate risk from AI," said Charles Radclyffe, head of AI at Fidelity International.
This collaboration builds upon the recently announced joint efforts 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 Expands Partnerships to Help Enterprises Accelerate the Path to AI
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 dramatically 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.
At Grafana, we believe AI is a big growth opportunity for observability, said Tom Wilkie, VP of Product at Grafana Labs. With Dotscience, the process for training AI models is simplified. The integration with Grafana enables data science teams to monitor these trained models in production continuously. By bringing DevOps practices to ML, data science and ML teams can eliminate silos, maximize productivity and minimize MTTR if there are issues with a model that is being observed.
In a separate press release today, Dotscience also announced a native GitLab integration. 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 Increases Accessibility to Growing MLOps Ecosystem with Added Multi-Cloud Support
The Dotscience platform is available as SaaS or on-premises and empowers ML and data science teams in industries including fintech, autonomous vehicles, healthcare and consultancies to achieve reproducibility, accountability, collaboration and continuous delivery across the AI model lifecycle.
Dotscience is now available on the AWS Marketplace, enabling AWS customers to easily and quickly deploy Dotscience directly through AWS Marketplaces 1-Click Deployment, and through Microsoft Azure.
Finding the right software to meet your specific business needs can be challenging, particularly for data scientists and machine learning teams for whom the options have been limited, said Marsden. Extending the installation possibilities of Dotscience to include AWS and Azure gives more companies access to an integrated ML platform that provides the unified version control and collaboration these teams need to simplify, accelerate and control AI development.
Dotscience Expands Frameworks in Which Data Scientists Can Deploy ML Models
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 Dotsciences recently added deploy and monitor platform advancementsthe easiest way to deploy and monitor ML models on Kubernetes clustersavailable to data scientists using a greater range of ML frameworks.
A key principle for Dotscience is that it has always been agnostic in terms of the exact framework data scientists can use for ML development, continued Marsden. As a next natural step in our product progression, we are enabling data scientists to deploy on their preferred ML framework.
In a separate press release today, Dotscience announced that leading open banking API provider, TrueLayer, has deployed Dotscience to enable reproducibility, provenance and metric tracking of AI models.
Dotscience, the pioneer in DevOps for machine learning (MLOps), brings DevOps principles followed by high-performing software teams to ML and data science. The Dotscience software platform for collaborative, end-to-end ML lifecycle management empowers ML and data science teams in industries including fintech, autonomous vehicles, healthcare and consultancies to achieve reproducibility, accountability, collaboration and continuous delivery across the AI model lifecycle. Founded in 2017 by container storage veteran Luke Marsden, Dotscience is headquartered in the UK with offices in the US. Its mission is to accelerate and unlock the true value of data and analytics assets through AI.
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