Databricks Offers Automation Throughout the End-to-End Data and Machine Learning Lifecycle

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A recent press release reports, “Databricks, the leader in Unified Analytics and original creators of Apache Spark, today announced that its Unified Analytics Platform now offers 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.”

The release continues, “Gartner predicts by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader use by citizen data scientists. To accelerate this automation and help data science teams provide value to their business, 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.”

Adam Conway, vice president of product management at Databricks, noted, “Data scientists and machine learning engineers are continuously looking for ways to accelerate and scale their machine learning initiatives… 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.”


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