Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
Bargava Subramanian

Bargava Subramanian
Machine Learning Engineer, Independent

Website | @bargava

Bargava Subramanian is a machine learning engineer based in Bangalore, India. Bargava has 14 years’ experience delivering business analytics solutions to investment banks, entertainment studios, and high-tech companies. He has given talks and conducted numerous workshops on data science, machine learning, deep learning, and optimization in Python and R around the world. He mentors early-stage startups in their data science journey. Bargava holds a master’s degree in statistics from the University of Maryland at College Park. He is an ardent NBA fan.

Sessions

1:30pm5:00pm Tuesday, December 5, 2017
Design, UX, visualization, and VR, Machine Learning
Location: 310/311 Level: Beginner
Bargava Subramanian (Independent), Amit Kapoor (narrativeVIZ Consulting)
One of the challenges in traditional data visualization is that they are static and have bounds on limited physical/pixel space. Interactive visualizations allows us to move beyond this limitation by adding layers of interactions. Bargava Subramanian and Amit Kapoor teach the art and science of creating interactive data visualizations. Read more.
11:15am11:55am Wednesday, December 6, 2017
Data engineering and architecture, Machine Learning
Location: Summit 1 Level: Beginner
In the current Agile business environment, where developers are required to experiment multiple ideas and also react to various situations, doing cloud-native development is the way to go. Harjinder Mistry and Bargava Subramanian explain how to design and build a microservices-based cloud-native machine learning application. Read more.
1:45pm2:25pm Wednesday, December 6, 2017
Data science and advanced analytics, Machine Learning
Location: Summit 2 Level: Intermediate
Bargava Subramanian and Harjinder Mistry share data engineering and machine learning strategies for building an efficient real-time recommendation engine when the transaction data is both big and wide. They also outline a novel way of generating frequent patterns using collaborative filtering and matrix factorization on Apache Spark and serving it using Elasticsearch in the cloud. Read more.