This time we team up with GeekOut and Topia to get you the sharpest minds of the tech industry presenting their insights on what’s the hottest and freshest in tech world.
The event is kindly held at Topia, Lõõtsa 12, 12th floor.
Sara Robinson
Developer Advocate on Google’s Cloud Platform team
Text analysis: from pre-trained APIs to custom models
Most applications deal with text data in some form: from chat messages to product reviews to comments and more. Making sense of large amounts of text data manually is difficult and time consuming. Luckily, advances in natural language processing (NLP) have given us a variety of tools to help analyze text. Whether you’re new to ML or you’ve already built your own custom model, by the end of this talk you’ll know how to get started adding NLP to your own application. I’ll begin by introducing the Cloud Natural Language API, which lets you utilize a pre-trained model to get insights from your text with a single API call. Then I’ll discuss how to build a custom model trained on your own data using TensorFlow and Cloud ML Engine, with lots of live demos along the way.
Chris Thalinger
Software engineer working on Java Virtual Machines for over 13 years
Twitter’s quest for wholly Graal
Twitter is a massively distributed system with thousands of machines running thousands of JVMs. In any similar big system a small change in performance and CPU utilization is multiplied thousandfold and results in big savings. Electricity costs, cooling costs, and possibly reduction of server farm size. One way to improve Java performance and reduce CPU utilization is to simply generate better machine code. Simply is obviously not trivial but doable. Twitter is going down that road and experimenting with Graal to generate better code and reduce cost.
