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Wednesday, November 6 2019
6:30pm - 9:00pm

Enterprise Machine Learning & Data Science

Agenda: 6:30 pm - Arrival and networking7:15 pm - Welcome by WiMLDS & Databricks7:20 pm - Melissa Kilby & Melody Wolk (Apple) - Hackers Only Have to be Right Once: Using Machine Learning to Defend All the Things, All the Time7:40 pm - Brooke Wenig (Databricks) - Lessons from the Field: Data Science8:00 pm - Maggie Chu (Databricks) - MLflow: An open platform to simplify the machine learning lifecycle8:20 pm - Networking9:00 pm - Departure Food and drinks will be provided by our host, Databricks. About Melissa: Melissa Kilby is a Sr. Research Scientist at Apple. She is part of a cross-functional team of researchers and engineers in Apple Information Security. Melissa is passionate about tackling new problem spaces in information security and machine learning.www.linkedin.com/in/melissackilby/ About Melody: www.linkedin.com/in/melodywolk/ Melissa & Melody's Talk - Hackers Only Have to be Right Once: Using Machine Learning to Defend All the Things, All the Time Melissa and Melody invite you to the world of cyber security and hacking. The tech talk features an exciting simulation of an attack, followed by a deep dive from a defender’s perspective showing some of the ways machine learning can be used to protect an organization at massive scale. About Brooke: Brooke Wenig is the Machine Learning Practice Lead at Databricks. She guides and assists customers in implementing machine learning pipelines, as well as teaching Distributed Machine Learning & Deep Learning courses. She received an MS in Computer Science from UCLA with a focus on distributed machine learning. She speaks Mandarin Chinese fluently and enjoys cycling.www.linkedin.com/in/brookewenig/ Brooke's Talk - Lessons from the Field: Data Science Machine learning has transformed the way we consume data, but yet, a terrifying 85% of data science projects fail. In this talk, we will share machine learning best practices learned from working with Databricks customers on ML use cases across various industries on what to do (and what to avoid). We will cover how to setup machine learning initiatives for success, how to address common challenges, and share customer success stories along the way. About Maggie: Maggie Chu is a Solutions Architect at Databricks. She has worked in the technical customer-facing role for three years in sales and support - presenting, scoping out and delivering tailored solutions to customers in data architecture, Spark, and machine learning. Before moving to San Francisco, Maggie worked in tech and education in Asia.www.linkedin.com/in/mychu92/ Maggie's Talk - MLflow: An open platform to simplify the machine learning lifecycle Developing applications that successfully leverage machine learning is difficult. Building and deploying a machine learning model is challenging to do once. Enabling other data scientists (or even yourself, one month later) to reproduce your pipeline, compare the results of different versions, track what’s running where, and redeploy and rollback updated models is much harder. This talk presents an overview of MLflow, a new open source project from Databricks that simplifies this process. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment and for managing the deployment of models to production. Moreover, MLflow is designed to be an open, modular platform—you can use it with any existing ML library and incorporate it incrementally into an existing ML development process.


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