Both methods seamlessly integrate with Snowflake, allowing users to jumpstart AI projects and deliver game-changing results quickly. AI Accelerators for Snowflake: Rapidly develop and deploy models with either low-code modular blocks and templates or a code-first approach.Snowflake Materialization: Simplify the AI lifecycle without sacrificing governance by materializing training datasets in Snowflake and eliminating data migration and duplication.Snowflake Monitoring: Monitor models deployed in Snowflake for drift, accuracy, or custom metrics with the ability to easily replace models after retraining.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.
#Vmware horizon monitoring code
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.The freedom to try complex and novel scenarios in a ‘recipe draft mode’ reduces the time spent moving and processing data back-and-forth. Data Preparation with Pushdown and Wrangler Enhancement: Prepare high-quality machine learning data while leveraging the scale and governance of the Snowflake Data Cloud.
“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.”ĭataRobot is joining Snowflake in mobilizing the world’s data to help organizations accelerate their AI lifecycles. “The DataRobot AI Platform and the Snowflake Data Cloud are a powerful combination,” said Venky Veeraraghavan, Chief Product Officer at DataRobot. LAS VEGAS–(BUSINESS WIRE)–DataRobot today 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. New integrations will enable joint customers to accelerate their end-to-end AI lifecycle
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