Integration of H2O.ai and KNIME offerings enables enterprises to create more accurate predictions and reduce time to value when implementing data science projects
KNIME and H2O.ai, the two data science pioneers known for their open source platforms, today announced a strategic partnership that integrates offerings from both companies. The joint offering combines Driverless AI for AutoML and KNIME Server for workflow management across the entire data science life cycle – from data access to optimization and deployment. With this partnership, KNIME and H2O.ai offer a complete no-code, enterprise data science solution to add value in any industry for end-to-end data science automation.
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Preparing data for AI, selecting the right model, pushing it into production, and continuously optimizing it is a process that typically requires many stakeholders and several tools. Parts of it can be automated, but flexibility is paramount to select the techniques that answer a company’s questions in the best way. The lack of an end-to-end tooling prevalent in most data practices also makes it very difficult to ensure data lineage. This H2O.ai and KNIME integration now provides a solution that covers all these challenges as well as increases data scientists’ productivity, reduces overall IT spend, and creates and uses more accurate predictions.
The expanded integration between H2O.ai and KNIME brings together all-encompassing, intuitive, automated machine learning from H2O.ai with the guided analytics from KNIME.
Customers of H2O.ai and KNIME can now:
- Develop an integrated data science workflow in KNIME Analytics Platform and KNIME Server, from data discovery and data preparation to production-ready predictive models.
- Deliver the power of automatic machine learning to business analysts, enabling more citizen data scientists with H2O Driverless AI.
- Reduce model deployment times, leveraging H2O Driverless AI and KNIME Server for reliably managing the workflow and creation process in production.
“We have been using KNIME and H2O Driverless AI for years, and we are very excited about this new integration and the automation and simplification that it will bring to our data science workflow,” said Alejandro Lopez, data science leader of Vision Banco.
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“H2O Driverless AI users can now get an integrated data access and preparation platform with KNIME. This allows seamless operationalization and continuous learning demanded by our customers adapting at the speed of change today,” said Sri Ambati, CEO and founder of H2O.ai.
“The integration of Driverless AI offers KNIME users a strong, additional option to automate machine learning out of the box with a huge range of powerful algorithms. We believe that flexibility of choice brings most value to our users and customers, and H2O is a great addition to the mix,” said Michael Berthold, CEO and co-founder of KNIME.
H2O is a leading open source AI platform, and its Driverless AI is a leading automatic machine learning (AutoML) platform. H2O Driverless AI automates time-consuming machine learning workflows with automatic feature engineering, model tuning, and model selection to achieve the highest predictive accuracy within the shortest amount of time. H2O Driverless AI empowers data scientists, statisticians and domain scientists to work on projects faster and more efficiently by using automation to complete tasks that can take months in minutes or hours. It can now be used within a KNIME workflow.
KNIME Analytics Platform and KNIME Server provide a visual workflow platform for ETL, further machine learning choices, deployment, collaboration, and cloud execution. Users can blend and transform data from hundreds of data sources using a visual, no-code, fully auditable approach. KNIME also offers a wide range of options for how the output can be deployed — from REST to web applications, BI dashboards, and other third-party tools. With Integrated Deployment, teams can automatically and continuously deploy and update models including the process of data access and preprocessing. Driverless AI adds a powerful choice for automating machine learning.
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