Understand the project#

First take a moment to understand the goals for this quick start and the data at hand.

Objectives#

Rather than designing new elements like in the quick starts for data preparation, machine learning, or MLOps, this quick start focuses on how to collaborate with colleagues and use the AI capabilities they have already created as inputs for your own objectives.

In this quick start, you’ll:

Understand a project’s objectives by reviewing the Flow.
Recognize how group assignments impact project security.
Communicate insights with a dashboard.
Run a colleague’s workload by using both an automation scenario and a Dataiku application.

Note

All capabilities featured in this quick start can be completed with the AI Consumer user profile.

Tip

To check your work, you can review a completed version of this entire project from data preparation through MLOps on the Dataiku gallery.

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.

Dataiku has its own visual grammar to organize AI 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 at the items in the Flow now!

If not already there, from the () menu in the top navigation bar, select the Flow (or use the keyboard shortcut g + f).

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.

Use the right panel to review an item’s details#

To collaborate on a project, you’ll need to quickly get up to speed on what someone else’s Flow accomplishes. Try to figure out the purpose of this one.

At the far left of the Flow, click once on the job_postings dataset to select it.
Click the Details () icon in the right panel to review its metadata.
Click the Schema () icon underneath to see its columns.
Click Preview (or use the keyboard shortcut shift + p) to pull up the first few rows of data.
Beginning with the Prepare recipe at the start of the pipeline, review the recipes that transform the job_postings dataset at the beginning to the test_scored dataset at the end. Click once to select each one, and review the Details tab to help determine what they do.

Note

The model in this project happens to be a simple AutoML model. However, you can think of it as a placeholder for any kind of model — not only those built in Dataiku, but also custom models imported into Dataiku.

You could read the project’s wiki (use the keyboard shortcut g + w) for more information. However, from just browsing the Flow, you probably already have a good idea of what this project does. The pipeline prepares some data and builds a prediction model to classify a job posting as real or fake.

The readability of the Flow eases the challenge of bringing users of diverse skill sets and responsibilities onto the same platform. For example:

The Flow has visual recipes (in yellow) that everyone can understand, but also custom code (in orange).
Two interconnected Flow zones divide the Flow. Such zones can be useful for teams focused on different stages of a project.

Build the Flow#

Unlike the initial uploaded datasets, the downstream datasets appear as outlines. This is because no one has built them. In other words, no one has run the relevant recipes to populate these datasets. However, this isn’t a problem because the Flow contains the recipes required to create these outputs at any time.

Open the Flow Actions menu.
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.