Dataiku Guide-AI-powered data platform guidance

Example

A user needs help cleaning a dataset before analysis. Dataiku Guide provides a detailed workflow, starting from importing the dataset to applying cleaning techniques like removing duplicates, imputing missing values, and normalizing the data.

Scenario

A business analyst needs to preprocess a dataset to remove irrelevant data for a marketing analysis project. They use the step-by-step guidance to properly set up the cleaning tasks in Dataiku, ensuring data quality for further analysis.

Example

A user is unsure about the differences between regression and classification algorithms. Dataiku Guide explains the theoretical background of each, including the types of problems they solve and provides best practices on how to choose the appropriate model.

Scenario

A data scientist is designing a machine learning pipeline to predict customer churn. They’re debating between regression and classification. The guide’s explanation helps them understand why classification models are better suited for binary outcomes like churn prediction.

Example

A data engineer needs help setting up a real-time data pipeline. Dataiku Guide walks them through configuring the flow, setting up triggers, and handling real-time data streams, including using API nodes and streaming platforms.

Scenario

An enterprise needs to monitor real-time data feeds for fraud detection. The data engineer uses Dataiku to establish a pipeline that triggers alerts based on streaming data from financial transactions. Dataiku Guide assists in configuring the entire setup, from data ingestion to alerting.