Databricks, a provider of unified analytics and original creators of Apache Spark, is boosting its Unified Analytics Platform with automation and augmentation throughout the machine learning lifecycle.
The broader Augmented Analytics offering not only automates machine learning model building, but also extends to automated data preparation and model deployment. The new Automated Machine Learning (AutoML) capabilities empower expert and citizen data scientists alike.
Databricks’ Unified Analytics Platform is using machine learning to augment data preparation, visualization, feature engineering, hyperparameter tuning, model search, automatic model tracking, reproducibility, and deployment.
Centered around an integration with the open source framework MLflow, this AutoML offering enables citizen data scientists, not just experts, to augment their data science and machine learning workflows at scale.
“Data scientists and machine learning engineers are continuously looking for ways to accelerate and scale their machine learning initiatives,” said Adam Conway, vice president of product management at Databricks. “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.”
This offering provides AutoML capabilities at different levels of control and automation. Features include:
- AutoML Toolkit
- Automated Model Search
- Automated Hyperparameter Tuning
- Integration with AzureML
For more information about these updates, visit www.databricks.com.