Democratizing Machine Learning with the Power of Cloud

Getting Started with Visual Studio Tools for AI

Real-time DeepLearning on IoT Sensor Data

Romeo Kienzler

This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).

Azure Machine Learning and Data Journeys

Luca Mauri

Kubeflow and Data Science in Kubernetes

John Liu

Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John provides an overview of Kubeflow and how he has been using it in the wild.

Bootstrapping of PySpark Models for Factorial A/B Tests

A/B testing, i.e., measuring the impact of proposed variants of e.g. e-commerce websites, is fundamental for increasing conversion rates and other key business metrics. We have developed a solution that makes it possible to run dozens of simultaneous A/B tests, obtain conclusive results sooner, and get more interpretable results than just statistical significance, but rather probabilities of the change having a positive effect, how much revenue is risked, etc. To compute those metrics, we need to estimate the posterior distributions of the metrics, which are computed using Generalized Linear Models (GLMs). Since we process gigabytes of data, we use a PySpark implementation, which however does not provide standard errors of coefficients. We, therefore, use bootstrapping to estimate the distributions. In this talk, I’ll describe how we’ve implemented parallelization of an already parallelized GLM computation to be able to scale this computation horizontally over a large cluster in Databricks and describe various tweaks and how they’ve improved the performance.

The Internet of Things - PolarSeven

PolarSeven Pty Ltd

AWS Sydney Meetup Presentation by Binqi Zhang of PolarSeven - http://polarseven.com How the Internet of Things is expanding at a rapid pace with more and more connected devices being added all the time. The world technology landscape is ever changing and evolving as we can track and measure more data than ever before with high availability connectivity. Using AWS IoT, which is now available in Australia Binqi looks at some of the ways the new technology can be applied.

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Changing the Way Viacom Looks at Video Performance with Mark Cohen and Michae...

Video is everything at Viacom. They build their own video players on iOS, Android and web platforms, and they have to know how those players are performing so they track critical metrics in near real-time with Apache Kafka, Spark and the Databricks platform. In this session, Viacom will share how a quick proof of concept turned into a system that is giving them real insights into their video player performance. They will also discuss investigating platforms like Druid for fast slicing and dicing of data for business-oriented users. One of the key takeaways is learning how, as engineers, we should work to drive value through technology, even if we work for a company that may not be tech first. Also, the data you collect can be a distraction, so create focus. Lastly, different users require different interfaces into the same data; learn how Viacom made that happen through technology, even if we work for a company that may not be tech first. — The data you collect can be a distraction so create focus. — Different users require different interfaces into the same data. We’ll talk about how we made that happen.

Distributed Low Latency Micro Data Centers

Franziska Büttner

Managing your ML lifecycle with Azure Databricks and Azure ML

Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976

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