Databricks adds deep learning support to its cloudbased Apache Spark platform

Databricks, the company founded by the creators of the Apache Spark project, has announced the addition of deep learning support to its cloud-based Apache Spark platform. This enhancement adds GPU support and integrates popular deep learning libraries to the Databricks' big data platform, extending its capabilities to enable the rapid development of deep learning models. Data scientists looking to combine deep learning with big data -- whether it's recognizing handwriting, translating speech between languages, or distinguishing between malignant and benign tumors -- can now utilize Databricks for every stage of their workflow, from data wrangling to model tuning. Databricks is the first to integrate these diverse workloads in a fast, secure, and easy-to-use Apache Spark platform in the cloud.

Apache Spark and Deep Learning

The 2016 Spark Survey found that machine learning usage in production saw a 38 percent increase since 2015, making it one of Spark's key growth areas. Many leaders in machine learning, such as Yahoo, are choosing Spark for deep learning to achieve groundbreaking results with big data.

In March 2016, Databricks created and open sourced TensorFrames, a software library that enables the popular deep learning framework, TensorFlow to run on Spark. The newest enhancements simplify deep learning on Spark by adding out-of-the-box support for using TensorFrames with GPUs -- specialized hardware that can perform an impressive amount of deep learning-specific computations in parallel. With Databricks, data teams can easily conduct deep learning on highly optimized hardware with a few clicks or API calls.

End-to-End Deep Learning with Databricks

Databricks allows organizations to perform data wrangling, interactive exploration, stream data processing, and other advanced analytics techniques alongside deep learning in a comprehensive platform. By seamlessly combining these techniques on Databricks, organizations can avoid unwanted system complexities and simplify the development of deep learning.

Simplifications such as:

- More timely and accurate cancer detection for healthcare providers: To read and interpret pathology images with higher accuracy than humans

- Faster drug discovery for pharma: To predict therapeutic uses of drugs at earlier stages to speed up the development and sales pipelines

- More capable artificial intelligence, such as language translation: To translate spoken speech with computers at an accuracy that rivals human performance.





Read more: http://dbricks.co/db-deep-learning