Review the Flow#

One of the first concepts a user needs to understand about Dataiku is the Flow. The Flow is the visual representation of how datasets, recipes (steps for data transformation), and models work together to move data through an analytics pipeline.

See the Flow’s visual grammar#

Dataiku has its own visual grammar to organize AI, machine learning, and analytics projects in a collaborative way.

Shape	Item	Icon
	Dataset	The icon on the square represents the dataset’s storage location, such as Amazon S3, Snowflake, PostgreSQL, etc.
	Recipe	The icon on the circle represents the type of data transformation, such as a broom for a Prepare recipe or coiled snakes for a Python recipe.
	Model	The icon on the diamond represents the type of modeling task, such as prediction, clustering, time series forecasting, etc.

Tip

In addition to shape, color has meaning too.

Datasets are blue. Those shared from other projects are black.
Visual recipes are yellow. Code recipes are orange. LLM recipes are pink. Plugin recipes are red.
Machine learning elements are green.

Take a look now!

If not already there, from the left-most menu in the top navigation bar, click on the Flow (or use the keyboard shortcut g + f).

Important

This project begins in the Data Preparation Flow zone from a labeled dataset named job_postings composed of 95% real and 5% fake job postings. The pipeline builds a prediction model capable of classifying a job posting as real or fake. Your job will be to deploy the model found in the Machine Learning Flow zone as a real-time API endpoint.
Take a moment to review the objects in the Flow. Gain a high-level understanding of how the recipes first prepare, join, and split the data, then train a model, and finally use it score new data.

Tip

There are many other keyboard shortcuts beyond g + f. Type ? to pull up a menu or see the Accessibility page in the reference documentation.

Build the Flow#

Unlike the initial uploaded datasets, the downstream datasets appear as outlines. This is because they have not been built, meaning that the relevant recipes have not been run to populate these datasets. However, this is not a problem because the Flow contains the recipes required to create these outputs at any time.

Click to open the Flow Actions menu in the bottom right.
Click Build all.
Leaving the default options, click Build to run the recipes necessary to create the items furthest downstream.
When the job completes, refresh the page to see the built Flow.

Inspect the saved model#

Let’s take a closer look at the model found in the Flow.

From the Flow, double click to open the diamond-shaped Predict fraudulent (binary) model in the Machine Learning Flow zone.
Note that the model has only one version, and so this version is also the active version. As you retrain the model and deploy new versions, the history of model versions is tracked — making it easy to roll back between versions.
Click on the model version name Random forest (s2) - v1 to see the full report, including visualizations of its explainability and performance.
Return to the Flow (g + f) when finished inspecting the model.

Tip

In this case, the saved model in the Flow was built with Dataiku’s visual AutoML. However, it’s also possible to import models packaged with MLflow as saved models into Dataiku. See this blog on importing MLFlow saved models to learn more.