Data Science Investment Counter —Funding raised by UK data science companies in 2018.
£ 5.640 Million

Databricks unveils new machine learning features for automation

Artificial Intelligence company Databricks announced on Tuesday that its Unified Analytics Platform now offers automation and augmentation throughout the machine learning lifecycle.

The new platform’s features will not only automate machine learning model building but also data preparation and model deployment, thus making these new automated machine learning (AutoML) capabilities usable by both expert and citizen data scientists.

“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.

In fact, data from Gardner has predicted that by 2020, more than 40% of data science tasks will be automated, resulting in “increased productivity and broader use by citizen data scientists”.

To optimise the automation process, Databricks’ Unified Analytics Platform is using machine learning to enhance data preparation, visualisation, feature engineering, hyperparameter tuning, model search, automatic model tracking, reproducibility, and deployment.

AutoML capabilities are centred around integration with the open-source framework MLflow, a software developed to augment data science and machine learning workflows at scale.

Down below are some of the features provided by the Databricks’ Unified Analytics Platform, as mentioned in the latest press release from the company.

  • AutoML Toolkit
  • Automated Model Search
  • Automated Hyperparameter Tuning
  • Integration with Azure Machine Learning

“By introducing the concept of ‘low-code’ and ‘no-code’,” Conway said, “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.”

Databricks was founded by the original creators of Apache Spark in 2013 and aims to provide a Unified Analytics Platform for data science teams to collaborate with data engineering and lines of business to build data products. 

The company has so far raised $497 million from investors such as Andreessen Horowitz, Founders Future and others.

Image via Wikipedia.


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