| Sumario: | Thousands of the data scientists, analysts, engineers, developers, and executives converged at the Strata Data Conference San Francisco in March 2019 to absorb the insights and wisdom of the data world's best minds. The conference featured more than 300 speakers, 10 keynotes, 10 tutorials, and 150+ technical sessions. This video compilation captures the best from the conference, offering more than 100 hours of material to review at your own pace. Highlights include: The Strata Business Summit - speakers, executive briefings, and tech sessions laser focused on a central theme: How do the world’s leading companies build their successful data strategies? Learn about recommendation engines, AI-based personalization solutions, data governance, ML based customer insight harvesting, and more from data wizards like Zachery Anderson (Electronic Arts), Eric Bradlow (The Wharton School), David Talby (Pacific AI), Paco Nathan (derwen.ai), Jonathan Francis (Starbucks), and JoLynn Lavin (General Mills). The Strata Data Ethics Summit – Is your AI really making good decisions or have you built a deceptive black box that reinforces ugly stereotypes? Alistair Croll (Strata Chair), Tim O'Reilly (O'Reilly Media), and Susan Etlinger's (Altimeter Group) eight hour deep dive into the thorny issues of data and algorithms with help from Jana Eggers (Nara Logics), Rumman Chowdhury (Accenture), Kathy Baxter (Salesforce), Carole Piovesan (McCarthy Tétrault), and more. Hours of tutorials from the world's top data engineers, such as Francesca Lazzeri (Microsoft) and Holden Karau (Google) on training and deploying models with Kubeflow across different cloud vendors; Dean Wampler (Lightbend) on performing machine learning using Kafka-based streaming pipelines; and Jason Dai (Intel) on the Analytics Zoo, an analytics/AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline. Sessions devoted to Data Science, Machine Learning & AI, including Sharad Goel (Stanford University) on the challenges of "fair machine learning", which aims to ensure that decisions guided by algorithms are equitable; Kelley Rivoire (Stripe) on scaling machine learning using the Railyard API; Vinod Vaikuntanathan (MIT) on performing machine learning on encrypted data; and Jeremy Howard (platform.ai) on recent advances in deep learning that allow non-engineers to train neural networks from scratch without needing code or pre-existing labels. Sessions focus...
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