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Deliver More Models With Dataiku AutoML
Automated machine learning, also known as AutoML, aims to automate and simplify the ML model development process, making it more efficient and accessible to non-experts.
Dataiku helps with AutoML challenges for everyone, whether it's accessibility for non-experts to co-build AI projects or the ability for data experts to go faster when it comes to model building and testing.
Easily Customizable AutoML
Dataiku provides full AutoML capabilities, including automatic feature engineering, hyperparameter tuning, and model evaluation. Accept the default settings or easily modify any part for your specific objectives. You can easily choose the level of AutoML you’d prefer based on your level of technical understanding — whether prioritizing speed, interpretability, or performance.
Interpretable & Explainable AutoML
Often to comply with internal controls, regulatory reporting, or to ensure a lack of bias, many steps of the ML process are scrutinized. With AutoML, users may not be able to explain these steps or fully understand the impact of different variables.
Dataiku features various ways to explain model outputs (explainability), all in a visually guided way that empowers non-technical users while saving time for seasoned data scientists. As seen in the visual, individual predictions allow you to explore important features for a single variable, just one of several built-in interpretability options.
Built-In Controls for AutoML
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 and affecting business?
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.
Collaboration & Connection to Broader Work With AutoML
Often, 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 AutoML in Dataiku, it’s easy to connect to the bigger picture and establish best practices with the collaborative visual flow, which shows everything that’s occurred along the data pipeline. Search for previous relevant projects using Dataiku’s global search functionality to ensure the reuse of best practices.
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