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Putting the P in A(P)I: Why APIs are key to make AI scale - Tatiana Mejia
Oct
24
2:30 PM14:30

Putting the P in A(P)I: Why APIs are key to make AI scale - Tatiana Mejia

At Adobe we have more than 500 engineers and data scientists working on features that use machine learning and AI. In fact, AI is not just the fastest growing skill set, it is also one of Adobe's four innovation drivers. With Adobe Sensei, we want to democratize AI, so that intelligence can be part of every app, every tool, and every experience. The way to make AI scale at Adobe: APIs. We share our lessons learned in weaving AI into all our technology, what it takes to build an API layer for AI, and how to market AI at a Fortune 500 scale.

Tatiana Mejia leads product marketing for Adobe Sensei Services. She has over 15 years experience in machine learning, digital marketing, social marketing, and SaaS. Tatiana holds an MBA from the Stanford Graduate School of Business.

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APIs and DSLs for Building and Integrating Many Models - Harlan Harris (WayUp)
Oct
24
2:00 PM14:00

APIs and DSLs for Building and Integrating Many Models - Harlan Harris (WayUp)

Enterprise businesses often build separate, customized versions of vertical-specific models for each customer, requiring tight workflows and tooling to maintain velocity and quality. This talk describes the architecture and tools we built at EAB, an ed-tech company, to integrate many models predicting student graduation into an application. I'll provide guiding principles and how those led to use of a commercial API provider as well as a home-built DSL, a command-line workflow, and web apps for data and model validation.

Harlan D. Harris has a PhD in Computer Science/Machine Learning from the University of Illinois at Urbana-Champaign, and worked as a Cognitive Psychology researcher before turning to industry. He has worked at Kaplan Test Prep, the Advisory Board Company, WeWork and several startups in New York and DC. Harlan also co-founded the Data Science DC Meetup and Data Community DC, Inc., and co-wrote O'Reilly's Analyzing the Analyzers, a short e-book about the variety of data scientists.

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Model as a Service up and running in AWS - Lia Bifano (Nubank)
Oct
24
1:30 PM13:30

Model as a Service up and running in AWS - Lia Bifano (Nubank)

Predictive models has significant leverage in business value, such as finding the right product faster to a client, understanding customer behavior, personalizing customer service, among others. However if they aren’t deployed in a productive environment that allows to take decisions quickly, they might become useless. Besides that, it is important create an infrastructure that is possible retrain fast and supports A/B tests. The goal of this presentation is give a overview of best practices and show from scratch how a trained model can be deployed as a service in AWS. 
At the end of this presentation you’ll know what are the steps to deploy a model in AWS and we’ll walkthrough in reproducible scripts that deploys the model using docker and AWS command line clients. And of course, all the code is available in GitHub.

Lia is pursuing a Master's degree at EPFL and in the last two years she worked with Nubank team as a Data Scientist. At Nubank she developed predictive models and ETL pipelines and her previous work experience was at Itaú Bank as a Business Analyst. She has BS (2012) in Statistics from Unicamp and at university she worked with models to predict joined market volatility using copulas and with MCMC methods for Bayesian inference.

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