Dataiku Release Notes

Get started with Dataiku faster and take your workflows further. Integrate agents into operations while navigating projects with unprecedented ease.
Learn more in our release notes and find instructions to update your instance.

Dataiku 14.0 transforms how teams work, from the first day in Dataiku through deployment. Intuitive navigation and onboarding gets new users productive instantly, while advanced agent tools integrate AI deeper into enterprise workflows.

Ready to explore everything that’s coming up in Dataiku 14.0? Get an overview on this page to see what’s new in Dataiku 14, and then view our Release in a Box deck for all the details!

Highlighted Updates

• Completely Redesigned Homepage, Navigation, & Onboarding
• Third-Party and Custom Python Tools for AI Agents
• Full Monitoring Alerts in Unified Monitoring for Production Deployments
• Apply Your Corporate Branding to Charts, Stories, & Dashboards
• And a Few More Things

User Experience

Accelerate productivity with intuitive design

Completely Redesigned Homepage

Find what you need instantly with the completely refreshed Dataiku homepage. Projects, resources, and recent items are now easier to find right in the homepage. The Data Catalog is front and center on the homepage. Recent projects, objects, and documentation are now more visible, meaning no more digging through menus to discover critical items and datasets!

A New, Tailored Onboarding Experience

Now you can get teams productive faster with intelligent onboarding flows. Smart questionnaires deliver personalized quick starts, curated sample datasets, and guided tutorials that eliminate setup confusion. Teams will now start using Dataiku and immediately see the value within minutes.

GenAI & Agents

Now you can build tools that integrate directly with Jira and ServiceNow. These integrations enable your agents to create tickets, incident reports, and workflow automation through these third-party systems. You can also define custom Python tools that extend beyond built-in capabilities. Transform agents from simple chatbots into powerful enterprise automation engines.

AI Engineering Operations

Catch service failures before they derail operations. You can now create alerts straight in Unified Monitoring for the detection of deployed service issues. There is a new “Alerting” section that allows you to set up alerts on various statuses of your deployed projects and API endpoints. Go beyond manual checks and define triggers to keep your AI portfolio running at peak performance.

Analytics & Insights

Brand consistency meets analytical power. Apply your corporate fonts and colors across Charts, Dashboards, and Stories for professional presentations that reflect your organization’s identity. Every analytical output maintains your visual standards automatically.

And a Few More Things

• Enhanced Document Processing: Structured extraction from diverse file types powers more comprehensive knowledge bases
• Time Series Management: Advanced tracking in the Model Evaluation Store
• Data Steward Alerts: Automated notifications trigger on critical data changes
• Metadata AI Assistant: New AI Assistant that automatically generates comprehensive dataset metadata

Explore All Release Details In Dataiku Release Notes

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Dataiku 13.0 release notes

Dataiku 13.0 delivers machine learning capabilities, Generative AI updates, and additional integrations to Databricks and Snowflake for universal operations.
Learn more in our release notes and find instructions to update your instance.

Newer Releases

There are 5 new releases of Dataiku 13:

• Dataiku 13.1 delivers Generative AI updates, additional XOps capabilities, governance enhancements, and visualization updates
• Dataiku 13.2 delivers updates to Generative AI, the LLM Mesh, Data Preparation, AI & Machine Learning enhancements, visualization updates, and more.
• Dataiku 13.3 delivers updates to Deploy Anywhere capabilities, testing frameworks, Generative AI features, visualization enhancements, and user experience improvements.
• Dataiku 13.4 delivers AI agents, multimodal knowledge banks, GenAI budget controls, and text-to-SQL capabilities.
• Dataiku 13.5 delivers accelerated GenAI innovation, strengthened governance, and enhanced accessibility for all users.

Highlighted updates

• AI/ML: Multimodal ML
• Universal Ops: Deploy and Monitor in Snowflake
• Universal Ops: Export models to Databricks registry
• Universal Ops: Data Status in Unified Monitoring
• Universal Ops: Data Quality Ruleset templates
• Generative AI: LLM Mesh additions

Explore many more feature updates organized by Dataiku capability below.

Generative AI

Generative AI capabilities in Dataiku

LLM Mesh Improvements

Changes to the LLM Mesh allow users to access the latest and most powerful LLM models from various providers through the LLM Mesh’s managed connections – while ensuring secure and governed access to LLM resources through centralized administration and monitoring.
Knowledge Banks boost efficiency and collaboration across projects.
The LLM Mesh improvements introduce support for new models, connections, and features, including:

• New Mistral AI connection to support the latest API-based model
• Databricks DBRX Instruct model available in the Mosaic AI connection
• Ability to specify the “Organization ID” to the OpenAI connection
• Ability to share Knowledge Banks between projects
• Vertex AI & Databricks LLMs can be augmented with Knowledge Banks (bugfix)

Prompt Studios UX Improvements

The Prompt Studios UX has received several enhancements aimed at improving usability and efficiency:

• Prompt list in left tray is now collapsed by default, saving screen space and making it easier to work with long prompts and multiple examples, especially on smaller screens.
• Input field has been revamped to clearly distinguish between manually written test cases and cases from data sets.
• The interface now clarifies when the displayed results are no longer related to the current prompt settings, ensuring better context when developing & evaluating prompts.
• Users can now preview test cases from their input data sets before running the query, allowing for a better understanding of what types of values will be sent to the LLM prior to running the request.
• A new button has been introduced to automatically set a test case as an example, eliminating the need to retype inputs into the example window manually.
  These improvements streamline the workflow and enhance the overall user experience within the Prompt Studios.

AI/ML

Machine learning capabilities in Dataiku

New feature: Multimodal ML

Multimodal ML enables Dataiku users to develop models in AutoML that incorporate images, text, and tabular data. By leveraging the most suitable feature extraction and embedding models from LLM Mesh connections for each modality, users can extract the most relevant information from their features. This allows users to leverage the most recent and relevant embedding or extraction models from connections in your LLM Mesh.
By using embedding models from LLM Mesh connections, administrators can ensure secure, governed, and resource-effective development of multimodal models, resulting in cost savings and efficient computational usage.

Universal Ops

DataOps capabilities and MLOps capabilities in Dataiku

Snowflake for Deploy Anywhere & Unified Monitoring

Unified Monitoring removes siloes between platforms used to develop and productionalize models by providing a single interface to monitor pipeline health and oversee models from diverse origins.
With the new Model deployment to Snowflake, users can now utilize Snowpark Container Services to deploy their models developed in Dataiku. This integration allows for automatic status updates on activity, deployment, execution, and model drift monitoring on all models deployed in Snowpark Container Services, providing visibility and governance through Dataiku’s Unified Monitoring.

Data Status in Unified Monitoring

This new status in the unified monitoring dashboard indicates the data health or quality of the projects and pipelines deployed in Dataiku.
By combining operational and data quality statuses in a unified dashboard, operators and admins can easily identify potential data quality issues at a glance that may impact downstream processes or analytical insights.

Data Quality Ruleset Templates

This allows the copying and reusing of the newly released data quality rules.
Data quality rule templates allow users to save a group of rules as a packaged template and import these templates when creating new rules. All templates are accessible from the centralized instance-level data quality page, enabling easy sharing and reusability across datasets and projects.

Model Evaluation: Text Columns in Drift Monitoring

Support for monitoring drift in text columns has been added to the drift monitoring section in the Model Evaluation Store. Text-based drift metrics are displayed in a dedicated section of the model evaluation, providing visibility into potential distribution shifts in textual features between the reference and current datasets. This acknowledges the increasing prevalence of text data and enables more comprehensive drift analysis beyond just numerical and categorical features.

Model Export: Export model to a Databricks Registry

We have added the ability to export models directly to the Databricks registry from within Dataiku. Behind the scenes, the selected Dataiku saved model version is exported to MLflow format and automatically registered to your Unity Catalog or Databricks legacy registry, all within one seamless action. This feature is also available programmatically through the Dataiku Python API.

Visualization & Data Storytelling

Visualization capabilities in Dataiku

Publish a Model Evaluation Store to a Dashboard

Users can now publish their Model Evaluation Store (MES) directly to dashboards. This capability enables simple dashboarding for model monitoring by adding tabs from an MES to display the latest model evaluation results, including drift analysis, performance metrics, and other relevant insights. By consolidating this information in a centralized dashboard, teams can gain a higher-level, project-wide view of model monitoring and evaluation, facilitating better collaboration, documentation, and transparency across the organization. Furthermore, one or more MESs can be published to a workspace through these dashboards, providing a comprehensive MLOps view for a business initiative.

Charts Enhancements

Charts have received the following enhancements:

• Enable MIN/MAX aggregations for date columns for measures in 2 charts (KPI and pivot table), tooltips, and custom aggregations
• Scatter chart “connect the dots” to display the evolution of a numeric variable over time, with data points connected by straight line segments
• New zoom-by-rectangle selection and general performance improvements
• Chart gridlines
• Ability to change the default for empty values in pivot tables (0 for numeric, N/A for categorical)

Governance

Governance capabilities in Dataiku

Dataiku Govern Improvements

Dataiku Govern has received the following enhancements:

• Role assignment will be available at the item level.
  • Inheritance rules and permissions are not editable
• The roles and permissions tab is new in the left navigation menu.
• (Advanced Govern) Blueprint Designer now has conditional views, making the workflow content dynamic by displaying/hiding specific views depending on other fields’ selected values.

Resources to Support Dataiku Users

New resources available to Dataiku users:

• EU AI Act Readiness Program: to triage AI use cases by risk level and enforce step-by-step workflows that align with the new EU Act’s rigorous requirements
• Dataiku User Network Program: a program to send relevant content, event invitations, product updates and tips to Dataiku users
• Dataiku Launch Program: A series to support new users onboarding to Dataiku to understand the basics of creating a project, working in a dataset, automating, and collaborating in Dataiku
• Public Alteryx Quick Start: Alteryx users interested in transitioning to Dataiku can follow this Quick Start to get up & running in <30 minutes! It assumes no prior knowledge of Dataiku; users only need an internet connection.
• Unified Google Search (on Community, Academy, Knowledge Base, Developer Guide & Dataiku.com): Users can easily search across various Dataiku domains & receive consolidated results, making it easier to find solutions.
• Import Serialized Pipelines: Users can follow a new tutorial displaying how to save a model trained using code into a native Dataiku object for MLOps & model management.

Find all details in our release notes.

For previous releases

• Version 12.0
• Version 11.0
• Version 10.0
• Version 9.0
• Version 8.0
• Version 7.0
• Version 6.0

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Generative AI Capabilities

Build real and safe Generative AI applications at enterprise scale

LLM Mesh

The LLM Mesh provides the components in Dataiku that empower IT to take control and help teams build safe, secure, enterprise-ready GenAI applications. With dedicated components for AI service routing, PII screening, LLM response moderation, performance and cost tracking, and auditing of entire application flows, you get maximum control while delivering the performance your business expects.

AI-Assistants

Supercharge data prep, project explanations, and coding tasks with AI assistants.

AI-Prepare

With Dataiku’s AI Prepare assistant, simply describe the transformation you want to apply and the AI assistant automatically generates the necessary data preparation steps. The ability to modify both your prompt and the resulting steps means you can prepare data faster than ever, yet still stay in complete control.

AI Explain

Unsure what’s happening in a data pipeline? Use AI Explain and powerful LLMs to automatically generate descriptions that explain Dataiku Flows or individual Flow Zones. Say goodbye to tedious documentation and reverse engineering cycles, and hello to your new favorite feature!

AI Code Assistants

Experience AI Code Assistants in Dataiku! Submit a code-related question or magic command through AI Code Assistant and receive context-enriched answers. This handy feature helps you write, explain, or debug code, comment and document your work, create unit tests, and more.

Prompt Studios & Recipe

With Prompt Studios in Dataiku, iteratively design and evaluate LLM prompts, compare performance and cost across models, and operationalize Generative AI in your data projects.

Retrieval Augmented Generation

Chatbots powered by generic LLMs can save time for common queries, but are unable to access recent data or critical internal documentation and so may miss out on key details.
By applying Retrieval Augmented Generation (RAG) and semantic search techniques in Dataiku, you can augment foundational LLMs with your own knowledge base to ensure chatbots provide the most relevant, accurate, and trustworthy information possible.

LLM-Powered NLP Recipes

Updating traditional NLP pipelines with modern Generative AI techniques is fast and easy with out-of-the-box, visual components. Dataiku offers no-code text recipes enhanced with pre-trained HuggingFace models and LLMs for text summarization, classification, sentiment & emotion analysis, and other common language tasks.

Increase Transparency

Help Everyone Understand AI Projects and Outputs

Auto Feature Generation

Improve efficiency and model performance by automatically generating new features from your existing datasets in a fraction of the time. Learn more in the Academy Knowledge Base article and in the reference documentation.

Universal Feature Importance

Model-agnostic visualizations for feature importance provide consistent & comparable explainability for models of all types. Learn more in the Academy Knowledge Base article and in the reference documentation.

Uplift Modeling

Apply this casual machine learning capability to measure cause & effect relationships and estimate an intervention’s impact on outcomes. Learn more in the Academy hands-on tutorial and in the reference documentation.

Data Quality

With Dataiku, you gain a visual and permanent understanding of your data quality issues. With data quality rules, experience a new and improved way to proactively monitor data quality issues. Anyone from data engineers to analysts can quickly set up checks for certain parameters.

Standardize Components

Ensure Success With Best Practices and Approved Components

Help Center

Quickly access reference documentation, educational materials, and personalized dynamic content recommendations without ever leaving the screen you’re on. Learn more in the reference documentation.

Data Catalog

Custom data collections and a central place to browse all of your organization’s connected data make it easier to discover high quality data to use in your projects. Learn more in the Academy Knowledge Base article and in the reference documentation.

New Dataiku Solutions

Accelerate speed to value for industry proven use cases with pre-built project templates and plug & play applications. Recently developed solutions include Process Mining, Financial Forecasting, Credit Scoring, Batch Performance Optimization, Pharmacoviligance, Social Determinants of Health, and Product Recommendations. Learn more and browse additional solutions in the Dataiku solutions catalog, or explore projects directly by searching for “solution” in the Dataiku Gallery.

Centralize Operations

Deliver Projects With Consistent Deployment and Management

Unified Monitoring

Unified Monitoring is the central hub for overseeing and monitoring pipelines and models developed and deployed across diverse platforms. It enables operators to extend coverage using External Models and Deploy Anywhere capabilities, and more -consolidating monitoring for deployments, projects, and APIs across various platforms.

Deploy Anywhere

Deploy models to other production environments like AWS SageMaker, Azure ML, and Google Vertex. This gives teams the flexibility to develop a model in one place but deploy in another, all while leveraging Dataiku as a central location to monitor, govern, and democratize access to all models.

External Models

You can now utilize existing AWS Sagemaker, Microsoft AzureML, or Google Vertex AI models in Dataiku. By integrating external models, you can leverage the benefits of traditional Dataiku models for models deployed externally.

Model Overrides

Ensure safe predictions by applying guardrails on models via business rules that enforce outcomes for known cases. Learn more in our Academy hands-on tutorial and in the reference documentation.

Enhanced Deployment & Monitoring

Additional drift metrics and streamlined deployment & monitoring setup for more efficient operations. Learn more in the reference documentation.

New Governance Views

A Kanban board and synchronized deployment details allow operations and leaders to assess the status of all governed projects at a glance. Learn more in the reference documentation.

Schema Management and Flow Build Improvements

Enhanced options and default settings for building recipes, datasets, and Flow Zones, engine selection, and schema propagation make it easier than ever for designers and operators alike to refresh and maintain data pipelines. Learn more in the Academy Knowledge Base article and the reference documentation.

Find all details in our release notes.

For previous releases
Version 11.0
Version 10.0
Version 9.0
Version 8.0
Version 7.0
Version 6.0

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Version 11.0 – July 12, 2022

Outcome Optimization

With outcome optimization and what-if accelerators, teams can systematically determine how to achieve the best possible result. Use this feature to find the optimal set of input values that will yield the desired prediction, given user-defined business constraints. Learn more in the reference documentation.

Managed Labeling System

Labeling data for computer vision use cases is often highly resource-intensive, requiring specialized knowledge or outsourced labor. With Dataiku’s built-in, collaborative managed labeling system, data annotation teams can efficiently generate mass quantities of high quality, labeled data for machine learning purposes. Learn more in the reference documentation.

Computer Vision

Make computer vision accessible to more data practitioners with visual deep learning tasks. Tackle object detection and image classification use cases 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. Learn more in the reference documentation.

Visual Time Series

New, visual time series capabilities enable teams to statistically analyze temporal data and develop, evaluate, and deploy forecasting models without needing code — all within the familiar Dataiku framework. Learn more in our Academy how-to article and hands-on tutorial and in the reference documentation.

Code Studios

Code studios are fully managed, isolated coding environments embedded into Dataiku projects where experts can craft their code using their preferred IDE or webapp stack. Learn more in our Academy how-to article and in the reference documentation.

Feature Store

Discover, explore, and reuse reference datasets containing curated features in the central feature store, and reduce the time data teams spend reinventing the wheel. Learn more in our Academy how-to article and in the reference documentation.

Programmatic Experiment Tracking

Capture and compare programmatic model experiments when developing a custom machine learning model, and seamlessly promote your selected external model as a Dataiku saved model. Learn more in the reference documentation.

Visual If Logic

New preparation processors, a switch formula, and a redesigned interface for constructing if…then…else expressions make it simpler for teams to recode data values and embed conditional logic in projects as business rules. Learn more in our Academy hands-on tutorial and in the reference documentation.

Flow Document Generator

Creating and maintaining documentation for ML pipelines is typically a time-consuming and tedious task. The flow document generator provides a quick and easy way to automatically create a detailed snapshot report listing information about what’s in your flow. Learn more in our Academy how-to article and in the reference documentation.

Seamless Sharing

With new project visibility options, quick object sharing, and access request workflows, teammates can more easily discover data assets and request access to reuse them elsewhere. Learn more in the reference documentation.

Model Stress Tests

Waiting until a model is live to assess its robustness to data quality issues can be risky. Run your model through a battery of stress tests simulating real world deployment conditions, prior to deployment. Learn more in the reference documentation.

Find all details in our release notes.

For older releases

Version 10.0

Version 9.0

Version 8.0

Version 7.0

Version 6.0

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Highlights of the latest Dataiku DSS releases. More details in our release notes.

Version 10.0 – November 2021

• Model Evaluation Store
  Use the Model Evaluation Store to capture and analyze historical performance metrics for machine learning models in production. Automated drift monitoring and built-in Model Comparisons make it easier than ever to monitor live models to ensure they are performing well and continuing to deliver relevant predictions. Learn more in the reference documentation.

• Model Comparisons
  Model Comparisons enable you to visually assess and compare the performance and configuration of multiple models at once, to aid in both model development and MLOps workflows. Compare saved models, historical model evaluations, and models in the Lab under development so at every moment you can make informed decisions about the best model to run in production. Learn more in the reference documentation.

• Govern
  Developing and deploying AI projects and models without proper oversight could result in poor performance and unintended impacts on customers and the organization. Achieve enterprise-grade governance and AI portfolio oversight with standardized project plans, risk and value assessments, a centralized model registry, and workflow management for reviews & sign-off. Learn more in the reference documentation.

• Workspaces
  Workspaces are a new collaboration channel between analytics teams and the audiences they serve — a private space and single point of access where data consumers can easily find and review all the analytical assets they need from multiple Dataiku projects. Navigate to Workspaces via the main Applications menu, or even set Workspaces as your Dataiku homepage. Learn more in our hands-on tutorial and in the reference documentation.

• Geospatial Analytics
  Take your Geospatial Analyses to the next level with a new GeoJoin visual recipe, PostGIS support, and numerous other geo-processors and visualization enhancements. Create buffers to a specified radius or extract administrative boundaries around geopoints, work with geometry types such as polygons and multi-line strings, plot location data on a density map, and more. Learn more in our hands-on tutorial and in the reference documentation.

• Assisted Data Exploration
  A smart assistant in the interactive visual statistics tab helps uncover variables of interest and automatically suggests visualizations to help users explore patterns and discover relationships between columns in a dataset. Learn more in the reference documentation.

• Visualization Enhancements
  Take your Dataiku visualizations to the next level with interactive dashboard filters and several additions to charting options, including customizable number formats, axis ranges, and color assignments. Learn more in our hands-on tutorial and in the reference documentation.

• Industry Solutions
  Off-the-shelf projects and use case-specific components can significantly accelerate development times and provide inspiration for teams. Dataiku’s Industry Solutions include ready-to-use objects and workflows that are fully customizable, extensible, and adaptable to business specifics. Browse the solutions catalog and learn more about solutions in the Dataiku knowledge base.

• Find all details in our release notes.

• Upgrade your Instance

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Highlights of the latest Dataiku DSS releases

Version 9.0 – March 2021

Visual ML Diagnostics

ML Diagnostics detect common machine learning problems and suggest design improvements, helping modelers identify and correct issues during the model development phase. Learn more in our hands-on tutorial and in the reference documentation.

Model Assertions

ML Assertions allow you to input subject matter expertise into model development to validate predictions and ensure your model is behaving intuitively. Learn more in our hands-on tutorial and the reference documentation.

Project Deployer

The Project Deployer makes it simple to promote project bundles from design into production environments, and manages deployment settings and versioning for all your Dataiku deployments in a centralized location. Learn more in our hands-on tutorial and in the reference documentation.

Smart Pattern Builder

The Smart Pattern Builder dynamically generates regular expressions for patterns you’d like to extract from your text data, based on examples you provide in the interactive tool.

What-if Analysis

Interactive Scoring allows you to run “what-if” simulations to experiment with different combinations of input feature values and review the resulting predictions. Access this feature within Visual ML, or publish the simulator to a dashboard for broader use. Learn more our hands-on tutorial for dashboard consumers, our how-to for ML practitioners, and in the reference documentation.

Fuzzy Join

Use the Fuzzy Join visual recipe to match non-exact data of many types, including strings, numbers, and GeoPoints. Learn more in the reference documentation.

Interactive Date Preparation and Filtering

Interactively filter your data by date ranges or date parts, and perform date calculations without writing formulas. Learn more in the reference documentation.

Find all details in our release notes.

For older releases
Version 8.0
Version 7.0
Version 6.0

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Highlights of the latest Dataiku DSS releases

More details in our release notes.

Version 8.0 – July 2020

• Dataiku Applications
  Dataiku Applications are a kind of DSS customization that allows you to reuse projects. Visual Applications allow you to package a project with a GUI on top, which empowers more people within an organization to leverage AI and self-service analytics. Learn more in our tutorial and in the reference documentation.

• Applications-As-Recipes
  Dataiku Applications are a kind of DSS customization that allows you to reuse projects. Applications-as-Recipes allow you to package part of a Flow into a recipe usable in the Flows of other projects. Learn more in our tutorial and in the reference documentation.

• Model Document Generator
  You can use the Model Document Generator to create documentation associated with any trained model. Learn more in the reference documentation.

• Flow Zones
  Flow Zones help you to organize large Flows so that they are easier to navigate. Learn more in our tutorial and in the reference documentation.

• Tag Categories
  Tag categories are an administrative tool to improve governance and consistency. They are set at a global level and apply across the entire instance. Learn more in our tutorial and in the reference documentation.

Find all details in our release notes.

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