Abstract: 

Today, data scientists use a rapidly evolving and diverse set of tools and platforms to
build advanced analytical models, and consequently they are seeking flexibility in picking their
own programming languages and modeling tools. After model development, operationalizing
these models and putting them in the hands of end users is the key to success for any data science
project. To this end, having a universal deployment platform that supports any type of model,
regardless of the tools or languages used, is a critical piece of the business value chain.
Ultimately, to generate value for their organization, business users should be able to apply these
models to their business problems in a self-service manner, drawing insights and making
decisions independently.

This session focuses on integrating data science workflows with Tableau using the
Tableau Analytics Extensions API. The session demonstrates how data scientists and developers
can take advantage of Analytics Extensions API to bring sophisticated analyses and machine
learning models into Tableau and enable business users to interact with these models
dynamically. Through dynamic interactions with advanced models, business users can easily
apply these models to their data and find the answers to their business questions quickly.

Bio: 

Amir Meimand is a Principal Solution Engineering on the Salesforce strategic solution team
focusing on Data Science and Machine Learning. Amir has 10+ years experiences in building,
deploying, and applying advanced analytics to solve enterprise business problems. Previously, he
was the director of Data Science at Zilliant, a SaaS company providing machine learning
solutions for price optimization and sales maximization lately acquired by Madison Dearborn.
Amir’s current area of focus is scaling advanced analytics solutions by democratizing data
science and machine learning. Amir holds a Ph.D. in Statistics and Operations Research from
Pennsylvania State University, 2013.