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Using deep learning to analyze IoT data to predict product issues

Vertical Use Case: Using machine learning to analyze IoT data to predict product issues
Technical Use Case: Data Ingest and ETL,Machine Learning,Deep Learning

"We were able to integrate Databricks with Tensorflow and Keras in a few clicks. And a 30+ node cluster would be up in less than four minutes."

— Prasad Chandravihar, Lead Data Scientist at Lennox International

Lennox International is an intercontinental provider of climate control products for the heating, ventilation, air conditioning, and refrigeration markets.

The Challenges

  • The sheer scale of their dataset did not allow them to process the data at the speed they required. As a result, they were forced to sample data from 15-20 devices and build models off of that subset of data

  • Their data teams have various skill sets (analysts, engineers, data scientists) and worked in silos which impeded productivity

  • Their data science team spent 80% of their day on data appropriation, data engineering, feature extraction, feature engineering, limiting their ability to train better and more accurate models


Customer Testimonial

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The Solution

Databricks provides Lennox International with a unified analytics platform on Microsoft Azure that simplifies data engineering and accelerates data science productivity.

  • Provides collaboration between team members

  • Automation and cluster management eliminates the need to tune and maintain Apache Spark™

  • Average time to start a cluster – 4 mins

  • Integrated H2O Sparkling Water, H20 Driverless AI, TensorFlow through GUI, and PowerBI

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