Combine Agility, Trust and Control to Scale AI with Success
Dataiku for MLOps
Designed for Production
Bring Greater Agility to ML Projects
Build real data pipelines that connect and use real data and computing resources so that you know how your models will behave in production.
A centralized system and standardized ML framework enable reusability of work across multiple projects. Visual flows provide visibility into each and every data manipulation that has occurred to data, regardless of who did it, for true version control.
Dataiku makes it easy to move projects into test and production environments with just a few clicks.
Set Up for Success With Model Expectations and Ready-to-Use Data
Gain Confidence in Shared Understandings
Set model expectations from the start by defining goals and then share it with stakeholders.
Leverage self-service data preparation to connect, discover, and cleanse targeted datasets.
Get up to speed on data quality by augmenting datasets with reusable features.
Test or Evaluate Your Models’ Fairness and Robustness
With Key Model Assessments in a Single Environment
Engineer models as a team, regardless of technical expertise, in one transparent system.
Assess model fairness and robustness with fairness metrics and subpopulation analysis.
Model stress tests are able to check model robustness and scalability. Improve understanding with counterfactual explanations.
Remove the Hassle From Deployment With a Centralized Hub
Quickly and Safely Deploy AI Artifacts Across Your Entire Ecosystem
Prevent poor models from being deployed. Review and approve AI models for production.
Bundle ML projects and create packages for quick deployment using Dataiku Deployer, reducing the time spent moving between design, testing, and production environments.
Deploy models programmatically via real-time APIs to automate ongoing projects using existing CI/CD tools like Jenkins and JFrog.
Automate Model Monitoring and Retrain Models When Required
With Model Evaluation, Model Comparison, & Drift Analysis
Get alerted to data & model drift in production when models deviate from training conditions and performance.
Ingest production audit logs from any source with equal ease. Create federated observability and monitor all your models, whether native to Dataiku or foreign, the same way.
Easily compare model performance versus initial expectations with automated indicators so you keep high-performing models in production or decide to retrain when needed.