Automated machine learning
Automatically build machine learning models with speed and scale
Easily build highly accurate machine learning models
Empower professional and non-professional data scientists to build machine learning models rapidly. Automate time-consuming and iterative tasks of model development using breakthrough research – and accelerate time to market.
Automatically build and deploy predictive models using the no-code UI or through a code-first notebooks experience.
Increase productivity with easy data exploration and profiling with intelligent feature engineering.
Easily create accurate models customised to your data and refined by a wide array of algorithms and hyperparameters.
Build responsible AI solutions with model interpretability, and fine-tune your models to improve accuracy.
Build models your way
Easily create models with the automated machine learning no-code UI or through a code-first notebooks experience. Customise your models quickly and apply control settings to iterations, thresholds, validations, blocked algorithms and other experiment criteria. Find the most accurate model for your data and then deploy it.
Improve productivity with automatic feature engineering and data visualisation
Visualise and profile your data to easily spot trends. Discover common errors and inconsistencies in your data through guardrails, and better understand recommended actions and apply them automatically. Utilise the automated feature selection and new feature generation capabilities to save time and build highly accurate models.
Enable efficient model creation
Automated machine learning intelligently selects from a wide array of algorithms and hyperparameters to help build highly accurate models. Use intelligent stopping to save time on compute, and prioritise the primary metric and sub-sampling to streamline experiment runs and speed results. Use built-in capabilities for common machine learning tasks such as classification, regression and time-series forecasting, including deep neural network support, to handle large datasets and improve model scores.
Understand models better
Built-in support for experiment run-summaries and detailed metrics visualisations help you understand models and compare model performance. Model interpretability helps evaluate model fit for raw and engineered features and provides insights into feature importance. Discover patterns, perform what-if analyses and develop deeper understanding of models to support transparency and trust in your business.