Coding for Cloud/Modelling intent of user using Probabilistic Machine Learning

Talk 1 by Harjinder Mistry

Abstract: In this talk, I will discuss how to design and build a microservices-based cloud-native machine learning application. We will discuss various challenges that we faced while writing cloud-native application. Some of them are:

Cloud means services. How to divide the application into a set of micro services ?

Cloud is a remote black box environment. How to debug and unit-test program ?

Cloud machine might have different setup. How to setup the required environment on cloud ?

Cloud is a cluster. How to write program in a cluster-native way ?

Cloud means variety of data sources. How to handle different data source ?

Cloud deployment seems scary. How to write deployment scripts ?

We discuss strategies on how to approach the above challenges, especially when the entire application is written in Python.

Talk 2 by Sarah Masud

Abstract : Understanding the user’s intent can help the product team dramatically improve the user’s experience. Be it adding the right products to a shopping cart, stocks to the portfolio or packages to a software stack, the user’s intent drives the choices and products added. When designing recommendation systems, modelling intent is non-trivial. The intent behind the action is hidden. This talk is about how the speaker used probabilistic machine learning to model intent.The talk discusses the common problem when modelling these kinds of problems from the start:

How to handle cold-start scenario? (no data)

How to automate intent identification?

Why Bayesian models?

About Speakers :

Harjinder Mistry is currently a member of Developer-Tools team in RedHat, where he is incorporating data science into next-generation developer tools powered by Spark. Prior to RedHat, he was a member of IBM Analytics team and he developed Spark-ML pipeline components of IBM Analytics Platform. Earlier, he had spent several years in DB2 SQL Query Optimizer team building and fixing the mathematical model that decides the query execution plan. He holds M.Tech. degree from IIIT, Bangalore, India.

Sarah Masud is an engineer at Red Hat where she works on developer-oriented analytic projects. Her bachelor’s thesis on Topics Modeling was presented at Ninth International Conference on Contemporary Computing. She is currently a mentor with the Next Scholars Program and the Global Give Back Circle. She also volunteers her time with Women Who Code and Systers review committees. She is ever enthusiastic about Data Science, Women in STEM, and Open Source.