New integrations will enable joint customers to accelerate their end-to-end AI lifecycle
DataRobot announced at Snowflake’s annual user conference, Snowflake Summit 2023, an expansion of the DataRobot and Snowflake integration, enabling organizations to build, deploy and scale AI models on Snowflake, maximizing their existing Snowflake investments.
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“As DataRobot continues to grow its broad enterprise ecosystem, we’re excited to see their generative AI capabilities expand as well.”
“The DataRobot AI Platform and the Snowflake Data Cloud are a powerful combination,” said Venky Veeraraghavan, Chief Product Officer at DataRobot. “Our integration with Snowflake for data preparation, model deployment, and monitoring brings the power of machine learning to where data resides, delivering an end-to-end enterprise-grade AI experience.”
DataRobot is joining Snowflake in mobilizing the world’s data to help organizations accelerate their AI lifecycles. New capabilities include:
- Data Preparation with Pushdown and Wrangler Enhancement: Prepare high-quality machine learning data while leveraging the scale and governance of the Snowflake Data Cloud. The freedom to try complex and novel scenarios in a ‘recipe draft mode’ reduces the time spent moving and processing data back-and-forth.
- DataRobot Notebooks with Snowpark: Code in your language of choice with fully managed and hosted DataRobot Notebooks, featuring Generative AI-based code generation, while pushing data processing to Snowflake using Snowpark.
- Snowflake Deploy with Snowpark: Deploy DataRobot models directly into Snowflake with a single click and generate secure predictions on sensitive data with in-database inference.
- Snowflake Monitoring: Monitor models deployed in Snowflake for drift, accuracy, or custom metrics with the ability to easily replace models after retraining.
- Snowflake Materialization: Simplify the AI lifecycle without sacrificing governance by materializing training datasets in Snowflake and eliminating data migration and duplication.
- AI Accelerators for Snowflake: Rapidly develop and deploy models with either low-code modular blocks and templates or a code-first approach. Both methods seamlessly integrate with Snowflake, allowing users to jumpstart AI projects and deliver game-changing results quickly.
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“The launch of these integrations illustrates DataRobot’s commitment to helping customers mobilize the world’s data on Snowflake,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “We look forward to driving deeper value for our joint customers by enabling them to quickly iterate, improve models and complete the ML lifecycle without repeated configuration, all while leveraging the security, scale, and performance of the Data Cloud.”
“The DataRobot-Snowflake native integration is so seamless that I don’t even have to think about it,” said Luke Bunge, Data Science Product Manager at Polaris. “We can train models faster and do it with more confidence that we’re following appropriate steps to get to a ML solution that’s going to deliver value. As Polaris continues to grow, we’re really excited to have DataRobot and Snowflake be a central part of that growth.”
“The seamless integration experience between DataRobot and Snowflake means customers can get more value from their data and better identify data signals,” said Tim Crawford, CIO Strategic Advisor at AVOA. “As DataRobot continues to grow its broad enterprise ecosystem, we’re excited to see their generative AI capabilities expand as well.”
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