Whether you’re a beginner looking for more guidance or an expert developing a custom ML model from scratch, Dataiku AutoML provides pre-built, configurable tasks to accelerate a wide range of common ML tasks.
Accept the default settings or easily modify any part of the AutoML journey for your specific objectives.
To expedite the feature engineering process, data scientists of all types — from citizens to experts — can leverage automatic feature generation or discover reference feature sets in Dataiku’s feature store and import them into their projects.
AutoML in Dataiku transparently applies handling strategies for feature selection and reduction, missing values, variable encoding, and rescaling based on data type. Accept the default settings or easily modify any part for your specific objectives.
AutoML isn’t just about producing basic models — with Dataiku, it can be simple, flexible, and powerful. Dataiku AutoML includes support for advanced techniques like deep learning, time series forecasting, causal prediction, and multimodal ML.
Learn More About Building Multimodal ML Use Cases With Dataiku
Though data scientists worked on the [NLP-based email categorization system], the team sped up development by leveraging Dataiku AutoML features to build and compare models quickly.
Air Canada leverages Dataiku’s visual flows, AutoML, and custom ML for their Customer 360 solution so they can easily and quickly fetch multiple potential features and train new predictive models, customer segmentations, and recommender systems in hours instead of weeks or months.
Both technical and non-technical users can better understand the outcomes of ML models thanks to robust explainability features in Dataiku for a white-box approach.
An extensive battery of interactive performance and interpretation reports includes fairness analysis, what-if analysis, stress tests, and more.
In giving non-experts access to ML, companies may feel like they’re giving up control for scale. How can leaders make sure that ML models in AutoML are good enough before using them?
In Dataiku, AutoML guardrails like debugging and built-in assertions alert you if your model behaves unexpectedly, while automated model documentation outlines what the model does, how the model was built (algorithms, features, processing, etc.), how the model was tuned, and performance.
AutoML can be isolated from broader projects with difficulty connecting together frameworks and tools for different parts of the ML process (i.e., data preparation, analysis, deployment, governance). This makes it difficult to see the bigger picture and can create silos that impact performance.
With Dataiku, everything happens in one tool so that stakeholders across the data and AI lifecycle have visibility and adhere to best practices. The collaborative, visual flow shows everything that’s occurred along the data pipeline for total transparency.
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Roel Dirks
Business Architect Data Science at Rabobank
