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Dataiku 11: Connect Your Experts With the Platform for Everyday AI

Demystify Time Series Forecasting

Empower Your Team With Forward-Looking Insights

Business professionals often encounter a technical knowledge barrier when faced with the highly specialized domain of time series analysis.

Dataiku’s built-in tools enable teams to statistically analyze temporal data and develop, evaluate, and deploy forecasting models using easy to understand, visual interfaces — all within the familiar Visual ML framework.

Go From Predictive to Prescriptive

Discover the Best Path to Optimize Your Outcomes

In order to deliver actionable insights and prescriptive recommendations to business stakeholders, data teams need a more systematic approach than trial and error to determine how to achieve the best possible result.

Outcome optimization accelerates manual what-if analysis by finding the optimal set of input values that will yield the desired prediction, given user-defined business constraints.

Code How You’re Most Comfortable

Experts Thrive Using Their Favorite Tools

While coders working on data and AI projects are usually most at ease in their own environment using their own tools, this can sometimes be at odds with an organization’s larger goals for analytics centralization and governance.

With embedded IDEs and robust experiment tracking for bespoke model development, advanced profiles are able to get the coding experience they prefer, but without the complications and overhead of custom setup.

Fully managed, isolated coding environments embedded into Dataiku projects, where experts can craft their code using their preferred IDE or webapp stack.

A central interface to store and compare all model runs made programmatically using the MLFlow framework.

Lower the Barriers to Deep Learning

Accelerate Computer Vision Use Cases From Start to Finish

From data annotation to deployment, developing deep learning models is technically challenging and resource intensive. As a result, computer vision projects are largely inaccessible to any but the most advanced data scientists.

With a built-in data labeling framework and visual interfaces for deep learning tasks, Dataiku enables more data scientists to take advantage of the latest in AI.

Tackle object detection and image classification with pre-trained models in the Visual ML interface and enjoy the frictionless training, experiment tracking, what-if analysis, and deployment experience that you would expect from Dataiku.

A collaborative, managed labeling system enables data annotation teams to generate mass quantities of high quality, labeled data for machine learning purposes.

Supercharge Collaboration and Reuse

Spend Less Time Starting From Scratch and Reuse What Works

When multiple team members independently create redundant data assets or reengineer the same features for various ML initiatives, it’s not only inefficient but also can lead to inconsistent results across projects.

With a centralized feature store and new object sharing workflows, Dataiku makes it easier for teams to safely reuse work and strike the right balance between discoverability and control.

A dedicated zone in Dataiku where teams can access and share reference datasets containing curated features suitable for reuse.

Facilitate project & asset discoverability and reuse with quick sharing and access request workflows.

Achieve Better Governance as you Scale AI

Gain Oversight and Control Over More Types of Projects

From developing project documentation for compliance or collaboration purposes, to ensuring models are appropriately tested before production, to gaining visibility over the hundreds of AI and analytics projects at a company, governing AI projects is challenging at scale. Dataiku makes it easier with:

Automatically generate a detailed report about the contents of your flow.

Bundles containing snapshots of pipelines and project artifacts are captured and versioned in a central registry, where they can be governed and managed according to a defined workflow.

Interrogate model behavior under real world deployment conditions prior to deployment.

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