[FREE] ODSC East 2019 Conference Meetup

This event requires registration at the link below: https://www.eventbrite.com/e/free-odsc-east-2019-conference-meetup-tickets-59411644829

We are excited to invite you to join together with your fellow Data Scientists on May 1st at ODSC East Conference Meetup - supported by Zepl (https://www.zepl.com/).

Schedule:
6:00 pm - 6:30 pm - Networking
6:30 pm - 7:10 pm - Session 1
7:15 pm - 7:55 pm - Session 2
8:00 pm - 8:40 pm - Session 3

Full Agenda:
Session 1 - Model Serving with Apache Zeppelin - 40 mins

Speaker: Moon soo Lee, CTO at Zepl https://www.linkedin.com/in/moonsoo-lee-4982a511/

Moonsoo Lee is the creator of Apache Zeppelin, with more than 300,000 users worldwide who have created more than 6 million notebooks. Moon is also the co-founder and CTO of Zepl, the data science platform that provides enterprise-grade support for Zeppelin and Jupyter notebooks.

Abstract:
Imagine that the production components of your system want to call a machine learning model in your notebook. You will quickly face problems around availability, stability, scalability of the notebook. And, because anyone can update the notebook at any time without automated unit tests, the workflow that notebook provides is not optimized for a production environment. Zeppelin 0.9.0 (upcoming release) will address these problems by providing a production-level notebook serving feature together with a new concept that lets you run unit tests on data science
notebooks. We’ll learn about bringing your machine learning model to production using Zeppelin with a live demo.

Session 2 - Data Modeling the Stock Market Today - Common Pitfalls to Avoid - 40 mins

Speaker:
Jess Stauth, Research Managing Director at Quantopian - https://www.linkedin.com/in/jessicastauth/

Dr. Jessica Stauth is the Managing Director of Portfolio Management and Research at Quantopian, a crowd-sourced quantitative investment firm, that inspires talented people from around the world to write investment algorithms. Jess and her team are in charge of selecting the algorithms from the Quantopian community, for our portfolio. Quantopian offers license agreements for algorithms that fit our investment strategy, and the licensing authors are paid based on their strategy's individual performance.

Abstract:
In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.

Session 3 - Deploying deep learning model at scale with Docker and Kubernetes - 40 mins

Speaker:
Heming Zhen, Data Engineer at Insight Data Science - https://www.linkedin.com/in/hemingzhen

Dr. Heming Zhen earned his bachelor’s degree in physics from University of Science and Technology of China before he came to the United States to pursue his Ph.D. in Medical Physics. Heming has conducted various research projects, including quality assurance in radiation therapy, cancer survival analysis, and machine learning in radiation therapy treatment planning. Heming is very interested in the latest Machine Learning/Deep Learning models.

Abstract:
In this presentation, Heming will demonstrate a project he built in 4 weeks as a Data Engineering Fellow at Insight Data Science. In part I, Heming will give a brief introduction to several key concepts and tools that are essential to the project, will highlight using TensorFlow Serving to serve a pre-trained model as an API, will review the use of container technology to distribute application while removing dependency issues and will discuss how to utilize Kubernetes to scale up container deployment with fault-tolerance. In Part II, Heming will demonstrate details on how he trained and deployed the model, the engineering challenges he faced when trying to scale up the system throughput, and how he used the aforementioned tools to overcome these challenges.