Come join us at the third event of our free technical meetup series, "Dynamic Talks", in Seattle/Redmond!

Dynamic Talks is an ongoing meetup series featuring technical talks from some of the leading experts in tech in major cities around the US. Enjoy talks about the most innovative subjects in: AI, ML, voice platforms, the Cloud and search. One of the objectives of Dynamic Talks is to foster and encourage the local technology community to share knowledge. Every event is free, with complimentary food and drinks.

Topic: "Machine Learning for Enterprise Operations"

Agenda

[6:00pm - 6:20pm]: Guests arrive, food and drinks are served

[6:20pm - 7:00pm]: First talk will be presented by Ilya Katsov on “Building an algorithmic price management system using ML”, followed by a Q&A

[7:00pm - 7:10pm]: Networking break

[7:10pm - 7:50pm]: Second talk will be presented by Tatiana Dashevskiy and Jeffrey Sewell on "ML Spark Jobs on-premises: Configuration and Setup of the Environment in Production and Building user-defined functions in PySpark and Processing Pipeline in Production", followed by a Q&A

[7:50pm - 8:00pm]: Another networking break

[8:00pm - 8:40pm]: Third talk will be presented by Scott Burger on "Attribution Modelling 101: Credit Where Credit is Due!", followed by a Q&A

[8:40pm - 9:00pm]: More networking, closing remarks, and the event ends

Ilya Katsov's talk details:

Title: "Building an algorithmic price management system using ML"

Abstract: In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.

About Ilya Katsov:

Ilya joined Grid Dynamics in 2009, and since then has been leading engagements with a number of major retail and technology companies. He is currently managing the Industrial AI consulting practice and 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).

Tatiana Dashevskiy and Jeffrey Sewell's talk details:

Title: "ML Spark Jobs on-premises: Configuration and Setup of the Environment in Production and Building user-defined functions in PySpark and Processing Pipeline in Production"

Abstract: We will discuss building Spark jobs on-premises, review YARN and Mesos Spark differences, performance optimization, the workflow of production of machine learning algorithms on-premises using PySpark. Two use cases will be considered: 1) building a model using MLlib in PySpark and 2) building model using user-defined functions in PySpark.

About Tatiana Dashevskiy:

Tatiana is currently a Senior Manager, Data Science, Corporate Strategy at T-Mobile. She has been with T-Mobile since 2017 and has worked on managing end-to-end ML model development, which includes data engineering, theory development using Python, testing and model deployment into production in a big data environment using Hive, Spark\PySpark. She has received a Ph.D in Physics from George State University and her focus was Dynamical System Analysis.

About Jeffrey Sewell:

Jeffrey Sewell is currently a Lead Technical Architect at Walt Disney. He previously worked at T-Mobile as a Principal Architect where he guided a team of developers on technical design, coding standards, and implementation. At T-Mobile, he also worked on the administration of the development and implementation of the Apache Spark-based graph processing system.

Scott Burger's talk details:

Title: "Attribution Modelling 101: Credit Where Credit is Due!"

Abstract: In this talk, Scott will go over the basics of Attribution Modelling, its advantages and disadvantages, and strategic ways to implement in the cloud leveraging big data.

About Scott Burger:

Scott Burger is a former Senior Data Scientist at Tableau Software in Seattle, and the author of the book "Intro to Machine Learning with R". A graduate in astrophysics from University College London, Scott uses cutting edge machine learning and AI principles in many different types of scenarios ranging from business intelligence to database optimizations.