| Sumario: | Strata San Jose 2018 offered thousands of top data scientists, analysts, engineers, and executives from around North America and the world with an opportunity to examine and absorb the best technologies and practices related to data engineering, architecture, machine learning, and AI. This video compilation provides a complete recording of the conference's keynote speeches, tutorials, and sessions, including unfettered access to the exclusive Strata Business Summit ("the missing MBA for data-driven business") and its Executive Briefings on how to turn data and algorithms into business advantage. You'll learn from featured speakers such as Google Brain Team leader Jeff Dean, Pinterest Senior VP for Engineering Li Fan, Cloudera CSO Mike Olson, Streamlio Cofounder Karthik Ramasamy, Atlassian Data Science Head Jennifer Prendki, and Jetlore Director of Algorithms Dorna Bandari. You'll hear keynotes from MapR Technologies' Anoop Dawar, Amazon's Alex Smola, O'Reilly Media's Ben Lorica, IBM's Dinesh Nirmal, and Cloudera's Amr Awadallah (Cloudera). And you'll pick up real world wisdom from in the trenches of big data engineering and analysis practitioners at General Mills, Kaiser Permanente, Ryanair, Procter & Gamble, BMW, Disney, ING, GE Digital, and more. Need more reasons to buy this compilation? Take a look at the breadth of topics covered at Strata (listed below) and remember: you can view it all (hundreds of speakers, 100+ hours of material) on your own schedule and at your own pace. Data Engineering and Architecture: 50+ sessions led by senior data engineers at Cloudera, AWS, Streamlio, and Confluent, and others that help you navigate the pitfalls of designing robust data pipelines, includes tutorials on building big data applications on AWS; time series data architecture; and using Impala to fix performance issues. Data Science and Machine Learning: 60+ sessions delivered by data scientists from Teradata, UC Berkeley RISE Lab, Microsoft, and more on the technologies that discover the hidden insights in your data, includes tutorials on building PyTorch based recommender systems; using R and Python for scalable data science, machine learning, and AI; and how to get started with TensorFlow. Big Data and Data Science in the Cloud (30+ sessions), includes a tutorial on running data analytic workloads in the cloud; Streaming Systems and Real-time Applications (20+ sessions), includes a tutorial on streaming applications as microservices using Kafka,...
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