WEBINAR: ODSC West Online Warm-Up (Free)

ODSC West is getting closer! We want to invite you to participate in ODSC West's Warm-Up.

To access this webinar, please register using the link below:
https://attendee.gotowebinar.com/register/3338809288646589441

After our great Part 1, we are bringing 4 new speakers from our ODSC West Conference to present 30 minutes sessions.

Matthew Rubashkin, Ph.D. AI Program Director at Insight Data Science: Building an image search service from scratch

Michael Mahoney, Ph.D. - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap

George Williams, Director of Data Science at GSI Technology, Inc: Visual Search: The Next Frontier of Search

Joshua Cook, Curriculum Developer at Databricks: Engineering for Data Science

Nisha Talagala, CTO/VP of Engineering at ParallelM: Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use

Full Agenda Detail

Session 1 - Building an image search service from scratch

Speaker:
Matthew Rubashkin, PhD

Abstract:
We are bringing a workshop on how you would go about building your representations, both for image and text data, and efficiently do similarity search. By the end of this workshop, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.

Session 2 - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap (30 Minutes)

Speaker:
Michael Mahoney, PhD

Abstract:
In this session, we will describe some of the underlying randomized linear algebra techniques. We'll describe Alchemist, a system for interfacing between Spark and existing MPI libraries that are designed to address this performance gap. We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system. We'll also describe related work on communication-avoiding machine learning, optimization-based methods that can call these algorithms, and extending Alchemist to provide an ipython notebook <=> MPI interface.

Session 3 - Visual Search: The Next Frontier of Search (30 Minutes)

Speaker:
George Williams

Abstract:
In this session, you will learn the latest state-of-the-art visual search research and techniques as the speakers will share their in-depth knowledge on the subject, how to scale your visual search solution to address the billion-scale problem and how to train models that provide more specific and accurate results for visually rich categories.

Session 4 - Engineering for Data Science (30 Minutes)

Speaker:
Joshua Cook

Abstract:
This talk will discuss Docker as a tool for the data scientist, in particular in conjunction with the popular interactive programming platform, Jupyter, and the cloud computing platform, Amazon Web Services (AWS). Using Docker, Jupyter, and AWS, the data scientist can take control of their environment configuration, prototype scalable data architectures, and trivially clone their work toward replicability and communication. This talk will toward developing a set of best practices for Engineering for Data Science.

Speaker:
Nisha Talagala

Abstract:
In this hands-on workshop, attendees will learn how to take Machine Learning and Deep Learning programs into a production use case and manage the full production lifecycle. This workshop is targeted for data scientists, with some basic knowledge of Machine Learning and/or Deep Learning algorithms, who would like to learn how to bring their promising experimental results on ML and DL algorithms into production success.