Interpretable Machine Learning for Enterprise Applications

Grid Dynamics is the engineering IT services company known for transformative, mission-critical cloud solutions for retail, finance and technology sectors. We architected some of the busiest e-commerce services on the Internet and have never had an outage during the peak season. Founded in 2006 and headquartered in San Ramon, California with offices throughout the US and Central and Eastern Europe, we focus on machine learning, AI, big data analytics, scalable omnichannel services, DevOps,and cloud enablement.

Grid Dynamics

How to extract important insights from the models? Which features might have a great impact on the model predictions?

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Join us on Thursday, August 6 at 11:00 am PDT/ 14:00 pm EDT to get the answers to these and many other questions. Hear from industry experts how leading companies are leveraging Interpretable Machine Learning for Enterprise Applications.

Key takeaways:<ul><li>Best practices for choosing modeling techniques</li></ul><ul><li>The ways to use a demand forecasting system to help drive strategic actions</li></ul><ul><li>Proven methods of building interpretable time series models</li></ul><ul><li>Networking with fellow data and machine learning industry leaders</li></ul>

<h3>Schedule:</h3>
11:00-11:20 AM PDT:AI/ML driven improvement of demand forecasts by Prabhakar Narasimhadevara, Director of Data Science, Advanced Analytics and Data Engineering, Stanley Black &amp; Decker
11:20-11:55 PM PDT :Interpretable Forecasting Models: Decomposition of Demand and Other Enterprise Time Series by Ilya Katsov, Head of Data Science, Grid Dynamics
11:55-12:05 PM PDT :
QnA session

About speakers:Prabhakar Narasimhadevara, Director of Data Science, Advanced Analytics, and Data Engineering, Stanley Black &amp; Decker (Atlanta, GA)Prabhakar is responsible for delivery of data engineering and analytics solutions in Stanley Black &amp; Decker organization. He has significant experience delivering analytical solutions in the healthcare, automotive, industrial distribution, industrial manufacturing, digital marketing, and services industries. Many of his projects involved Machine learning, artificial intelligence, advanced analytics, architecture, strategy development and implementation, process improvement, and data lake creation.Ilya Katsov is a Head of Data Science at Grid Dynamics. He joined Grid Dynamics in 2009, and since then has been leading engagements with a number of major retail and technology companies, focusing primarily on Big Data, Machine Learning, and Economic Modeling. He is currently leading the consulting practice that helps clients to become successful AI adopters and deliver innovative AI solutions. Prior to joining Grid Dynamics, Ilya worked at Intel Research on emerging wireless communication technologies. He is the author of several scientific articles and international patents, and also authored a book, “Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations” (2017).

Data Points is a series of online events, conferences, and meetups around Data Science, Artificial Intelligence, and Machine Learning in enterprise applications. Join other senior practitioners and executives for an immersive afternoon of talks on use cases, trends, and the technical infrastructure powering the most sophisticated data practices.Find us on social media:<a href="https://twitter.com/DataPointsSMT" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Twitter</a> <a href="https://www.linkedin.com/company/37059148" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">LinkedIn</a>

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