Press Releases

Dataiku Announces a Single Control Center for AI Governance and New Tools for MLOps

Designed for all stakeholders, Dataiku 10 empowers everyone from IT operations and domain experts to data scientists and risk managers

Dataiku Announces Global Virtual Event Focused on Responsible AI, MLOps, and AI Governance

Dataiku Product Days is a multi-day virtual event on December 2, 9, and 16 with more than 30 sessions, featuring experts from Deloitte, Capgemini, NVIDIA, and Snowflake

On Google Cloud Marketplace, Dataiku will further deliver the power of Everyday AI in the cloud

Dataiku Bets Big on Partner Ecosystem, Appoints New SVP of Ecosystems and Alliances, David Tharp

New partner program builds an “economy of opportunity” with Technology Alliances, Global Systems Integrators, and Channel Partners

Dataiku Recognized as a 2021 GartnerⓇ Peer Insights Customers’ Choice for Data Science and ML Platforms

Dataiku announced it was named a 2021 Gartner Peer Insights Customers' Choice for Data Science and Machine Learning Platforms.

Dataiku Raises $400M at a $4.6B Valuation

Dataiku, announced $400M in Series E investment led by Tiger Global to enable Everyday AI in the enterprise, with participation from several existing as well as new investors.

Dataiku Recognized by Snowflake as Data Science Partner of the Year

Dataiku was recognized by Snowflake, the Data Cloud company, as Data Science Partner of the Year this week at Snowflake’s virtual partner summit.

Total Economic Impact™ Study Finds Companies Using Dataiku’s Global Enterprise AI Platform See A 423% Return on Investment

The Total Economic Impact™ study shows Dataiku boosts the productivity of data scientists and engineers by 75%, among other benefits.

Dataiku Launches In AWS Marketplace

Dataiku, one of the world’s leading AI and machine learning platforms, announced today that it is available in AWS Marketplace. This availability opens the door for AWS customers to take advantage of Dataiku’s easy-to-use visual interface to build data pipelines, prepare data and build and deploy machine learning models on their cloud.