Deploy, monitor, and manage machine learning (ML) models seamlessly throughout the AI lifecycle with Dataiku. Centralized tools for model governance, monitoring, and collaboration ensure model accuracy, data quality, and consistency — whether managing a single model or scaling ML across the enterprise.
Deploy and manage models for both real-time and batch use cases across various infrastructures and environments.
Whether models require real-time predictions via APIs or scheduled batch processing, Dataiku ensures smooth deployments with full configuration to maintain operational stability.
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The Dataiku model evaluation store captures key metrics for visualizing model behavior over time. That means insights into when models might drift or deteriorate, allowing for proactive interventions.
With these metrics, teams can create checks and automate alerts for drift detection to take timely actions (e.g., retraining), keeping models accurate and aligned with business outcomes.
Dive Into How to Manage DriftWith Dataiku, maintain version control over all models and easily evaluate different versions through champion/challenger comparison. This ensures that model updates are intentional and measurable, supporting better decision making.
In case of performance issues or drift, teams can easily revert to previous versions, ensuring continuity and minimizing risks in production environments.
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MLOps in Dataiku integrates seamlessly with leading platforms (including AWS SageMaker, AzureML, Databricks, Google VertexAI, and Snowflake) for full, ecosystem-wide visibility to manage diverse projects without compromising on governance or control.
Dataiku Unified Monitoring provides a centralized view of model health and drift status, ensuring that teams stay informed on all deployments, no matter the infrastructure.
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MandM leverages the Dataiku deployment infrastructure and MLOps capabilities to deploy and monitor 100s of ML models in production with 10x faster operationalization vs. a code-only approach.
Before using Dataiku, the typical lead time for Western Digital’s object-detection model development and deployment project would take approximately one to two quarters. With Dataiku, the whole process only takes one to two weeks for a similar use case.
"We use [Dataiku] for data wrangling, for prototyping, for product development, but more importantly, as a deployment platform where we [have] easy-to-use MLOps and a production infrastructure around the models."
Dataiku simplifies model governance by automatically generating documentation, ensuring compliance and making every project reproducible. Dataiku allows users to conduct model stress-testing before deployment, helping teams validate performance and identify potential vulnerabilities — ensuring reliable outcomes.
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With the Dataiku Python API, IT teams and engineers can execute Dataiku tasks programmatically and integrate them into existing DevOps pipelines using tools like Jenkins, GitLabCI, Travis CI, or Azure DevOps.
Have continuous delivery by enabling model deployment and monitoring workflows to operate alongside established development practices, ensuring that AI projects remain agile and aligned with software engineering processes.
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