A shared environment for applied machine learning
Dataiku brings together data preparation, modelling and operational workflows in one platform.
Dataiku brings together data preparation, modelling and operational workflows in one platform.
In practice, many models stop at the point where they need to be reused or maintained.
Dataiku makes training, evaluation and deployment explicit. Models can be versioned, retrained and deployed through defined workflows, which reduces reliance on individual setups and makes it clearer which models are ready for use.
Which data was used? Who owns the model? When should it be reviewed or retrained?
Dataiku keeps this information close to the work itself.
Lineage, ownership and validation steps are visible without adding separate documentation or approval layers.
Early experimentation often needs flexibility. Over time, repeating the same work manually becomes a risk.
Dataiku allows experimentation to happen quickly, while gradually introducing shared patterns for data preparation, modelling and deployment. This supports progress without locking teams into rigid processes too early.
Dataiku is designed for companies that work with machine learning across multiple projects, roles and stages of maturity.
Its value lies less in individual features and more in how it brings different parts of the workflow together. The tool allows data preparation, feature creation, model training and deployment to live in the same environment.
Dataiku tends to make sense at a very specific point in the way machine learning is used.
Dataiku is usually a good fit when:
-Machine learning work is happening in more than one place
-Work needs to be reused, not just demonstrated
-More than one role is involved.
Data scientists, analysts and business stakeholders need to contribute without constantly handing work back and forth.
-Questions about responsibility are starting to surface
-You want structure without freezing how people work
The value depends entirely on how machine learning is actually used today and how that use is expected to change. We can explore that together.
It matters who built a model, which data it uses, and when it should be reviewed or retrained.
Not every analysis or model needs a platform. We help decide which use cases benefit from Dataiku and which are better handled elsewhere, so the tool has a clear purpose from the start.
Dataiku projects can quickly become dense. We help structure datasets, flows and naming so someone new can understand what exists, why it exists and how it fits together.
We support integrations with data warehouses, data platforms and external sources, making sure Dataiku works with prepared data rather than becoming another place where logic is duplicated.
We help define when an experiment becomes something others rely on. This includes versioning, validation steps and explicit criteria for deployment or review.
Models often fail because no one knows who owns them. We help make responsibility visible so models are reviewed, retrained or retired deliberately.
Dataiku usage changes over time. We support ongoing adjustments so structure evolves with the work, instead of being fixed too early or added too late.
Dataiku gives you many options. The difficulty lies in choosing which ones to use, and when.
We work with you to introduce structure only where it helps. When flexibility is needed, we leave room for it. When repetition appears, we standardise deliberately. This keeps the platform understandable for the people who depend on it, not just the people who built it.
The goal is not to “set up Dataiku,” but to ensure it continues to support real work as machine learning becomes more embedded.
Questions we often hear about working with Dataiku.
Dataiku is used to organise machine learning work that needs to be reused, reviewed or shared. It brings data preparation, modelling and deployment into one place so work does not stay scattered across notebooks, scripts and individual setups.
Dataiku is used by mixed teams. Data scientists typically handle modelling and validation, while analysts or domain experts contribute through visual workflows. How much non-technical users are involved depends on how the platform is set up.
Dataiku supports deployment workflows, but it does not replace thinking about how models are used downstream. It helps make deployment repeatable and visible, rather than fully automatic.
Dataiku typically consumes prepared data from a warehouse or data platform and focuses on preparation and modelling workflows. It usually does not replace core data infrastructure.
Explore how Dataiku can support your company with an i-spark expert.