Machine Learning through Google Cloud Platform’s Dataflow
Speaker : Maxime Legault Venne, JDA Labs
Machine learning aims to train models so they can predict accurate outcomes on unseen data. It requires training mathematical models based on a large amount of data with multiple different configurations to figure out which combination gives the best results, which can be a very long process. In order to solve that time problem, JDA leverages Google Cloud Platform's Dataflow to parallelize this processing in a reasonable amount of time.
Building a Dataflow streaming pipeline for sentiment analysis on Twitter
Speaker: Arsho Toubi, Google Cloud
In this meetup, you'll see how to run a Dataflow pipeline to perform sentiment analysis of Twitter status updates using the Google Cloud Natural Language API, then query and visualize those results. Google Cloud Platform’s Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Because Dataflow is a managed service, it can allocate resources on demand to minimize latency while maintaining high utilization efficiency. The Dataflow model combines batch and stream processing so developers don't have to make tradeoffs between correctness, cost, and processing time.
6:00 - 6:15: Welcome
6:15 – 7:00: JDA Labs - Machine Learning through Google Cloud Platform’s Dataflow
7:00- 7:30 : Google - Building a Dataflow streaming pipeline for sentiment analysis on Twitter
7:30 - 8:00: Networking + Q&A
The event is hosted and sponsored by JDA Labs & Google.
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