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Dataiku is pairing up with NYC Data Science Academy to host two presentations on the improtance of automation in business practice - and how to actually achieve it. Schedule: 6:30pm: Pizza + Beer & Networking 7:00pm: Automation: Maintaining your ML systems by Kasim Patel, Data Scientist at Dataiku 7:30pm: Coding Outside of IT: Lessons in Automation From Risk Reporting by James Long, VP of Risk Management at RenaissanceRe Abstracts: Automation: Maintaining your ML systems by Kasim Patel, Data Scientist at Dataiku Automation plays a critical role in moving data through data pipelines. Typical examples include automated nightly or weekly computations of similarity matrices for recommendation systems, retraining and deployment of machine learning models in fraud or churn cases, and so on. In this talk, Kasim will present some of Dataiku’s specific use-cases in automation, including monitoring of platform usage amongst organizations and automated support ticket tagging, all from within the Dataiku DSS platform. Coding Outside of IT: Lessons in Automation From Risk Reporting by James Long, VP of Risk Management at RenaissanceRe From XKCD cartoons to HBR articles, there's a recurring trope that automation is justified exclusively by time saved from turning manual activities into automated processes. JD will challenge this assumption and show there are many other benefits to business process automation which might be of much more value than simple time savings. He will also present lessons he's learned from increasing automation in the business by improving business analyst skills, tools, and attitudes toward process automation Bios: Kasim is a Data Scientist at Dataiku. He works on the strategy and growth team where he works with the company's own data to make Dataiku more data-driven. Before joining Dataiku, he worked as a Researcher in the Center for Brains, Minds and Machines (CBMM) at MIT. He holds an MS in Electrical and Computer Engineering from Boston University. JD Long is a native Kentuckian, an agricultural economist, insurance quant, stochastic modeler, and cocktail party host. He's an avid user of Python, R, AWS and colorful metaphors. JD is currently a risk management VP at the global reinsurer Renaissance Re. He lives in Jersey City NJ with his wife, a recovering trial lawyer, and their 11 year Roblox obsessed daughter.
Dataiku and General Assembly will be hosting two talks exploring the best practices and common setbacks teams run into when building ML systems into their infrastructure. Please RSVP on both Meetups and the GA website here: https://generalassemb.ly/education/bigger-problems-than-big-data-10-machine-learning-issues-with-twitter-cortex/new-york-city/70327 Priority will be given to those who have RSVP'd on both sites. Tentative Schedule: 6:30pm: Pizza + Beer 7:00pm: DS Best Practices (at Scale) with Jordan Volz, Senior Data Scientist at Dataiku 7:30pm: Bigger Problems than Big Data: 10 Machine Learning Issues that Nobody Talks About with Dan Shiebler, Senior Machine Learning Engineer at Twitter Cortex Abstracts: DS Best Practices (at Scale) with Jordan Volz, Senior Data Scientist at Dataiku: Although Data Science and Big Data are two worlds that are unwieldy on their own, their intersection has proven quite cumbersome for many businesses. In this talk, we will review some strategies for success in working with big and small data, common pitfalls in the data science process, building a collaborative data science experience, and how to overcome common obstacles when making the leap to large-scale data science. Bigger Problems than Big Data: 10 Machine Learning Issues that Nobody Talks About with Dan Shiebler, Senior Machine Learning Engineer at Twitter Cortex: In this presentation, we will explore the opportunities and growing pains of Machine Learning as a serious industry force. Through this exploration, we will learn how recent research in the Machine Learning space can enable large companies to become exponentially more productive in sharing and distributing Machine Learning models and insights. We will also see how Machine Learning systems can dramatically increase system complexity and technical debt. Bios: Jordan Volz is a Senior Data Scientist at Dataiku, where he helps customers design and implement ML applications. Prior to Dataiku, Jordan specialized in big data technologies as a systems engineer at Cloudera, and enterprise search technology as a technical consultant at Autonomy, frequently working with large financial organizations in the US and Canada. He holds degrees from Bard College and the University of Amherst, and is academically trained in pure mathematics. Dan works at Twitter Cortex, where he develops Machine Learning Models that make sense of the world's data. In his spare time he works with the Serre Lab at Brown University to train neural networks to think like humans. Previously, Dan designed smartphone sensor algorithms for car insurance at TrueMotion.