According to a press release on the company’s website, the Databricks Unified Analytics Platform will now offer automation and augmentation throughout the machine learning lifecycle. New augmented analytics features help to both automate machine learning model building and extend automated data preparation and model deployment. Automated machine learning (AutoML) functionality is designed for technical and non-technical users alike.
The Databricks Unified Analytics Platform uses machine learning to augmented data preparation, visualization, feature engineering, model search, automatic model tracking, deployment and more. An integration with open source framework MLflow enables citizen data scientists to augment their data science and machine learning workflows at scale.
The AutoML Toolkit acts as an automated end-to-end machine learning pipeline. It includes a variety of features and is available via Databricks Labs custom solutions. AutoML Toolkit executions are auto-tracked in MLflow. Automated Model Search enables optimized conditional hyperparameter search with enhanced Hyperopt and automated tracking to MLflow as well.
New Automated Hyperparameter Tuning features a deep integration with PySpark MLlib’s Cross Validation to automatically track MLlib experiments in MLflow. An integration with Azure Machine Learning allows customers access to the automated machine learning features inside Azure Machine Learning.
In a statement to Solutions Review, the company’s Vice President of Product Management Adam Conway said: “By introducing the concept of ‘low-code’ and ‘no-code’, AutoML represents a fundamental shift in the way organizations approach machine learning and data science. With the right automation, AutoML can dramatically shorten time-to-value for data science teams.”
Solutions Review recently named Databricks one of The 16 Best Predictive Analytics Software for 2019 and Beyond. Databricks also nabbed $250 million in new venture capital funding in February.
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