Artificial intelligence (AI) and machine learning have revolutionized how organizations are using their data. Automated machine learning (AutoML) facilitates and improves the end-to-end data science process. This process includes everything from preprocessing and cleaning the data, selecting and engineering appropriate features, tuning and optimizing the model, analyzing results, explaining and documenting the model, and of course, deploying it into production.

AutoML accelerates your AI initiatives by providing methods and processes to make machine learning accessible to both experts and nonexperts alike. Organizations looking to apply machine learning quickly and accurately without employing large numbers of data scientists can benefit from AutoML capabilities. For organizations that have data scientists, AutoML equips and empowers them to create more robust models with accuracy, speed, and transparency to deliver better performance and outcomes. In all cases, AutoML helps organizations quickly discover business value hidden inside their data and easily use that data to address complex problems.

H2O Driverless AI is a comprehensive automated machine learning product that uses AI to do AI, optimizing data science workflows to increase both the quantity and quality of data science projects delivered to business stakeholders. It empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in minutes or hours, not months.

H2O Driverless AI provides capabilities such as:

• Exploratory data analysis (AutoViz)
• Automatic feature engineering
• Model building and validation
• Automatic model documentation (AutoDoc)
• Model selection and deployment
• Machine learning interpretability (MLI)

AutoML does not replace machine learning operations (MLOps). AutoML focuses on automating and accelerating the model development portion of the ML pipeline, while MLOps provides an overall life cycle management framework for data preparation, model development, and coding. AutoML complements MLOps and can run successfully and efficiently with various MLOps frameworks such as cnvrg.io. MLOps provides an overall life cycle management framework for data preparation, model development, and coding.

With H2O Driverless AI bring-your-own recipes, and time series and automatic pipeline generation for model scoring, H2O Driverless AI provides companies with an extensible and customizable data science platform that addresses the needs of various use cases for every enterprise in every industry.