📙 Free book: Meet the Data Science Innovators
Was this email forwarded to you? Sign up here Learn from top data science leaders, who share their insights on their groundbreaking innovations, their careers, and the data science profession. Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business? These questions—and many more—are the focus of “The Data Science Innovator’s Playbook,” a free Domino Data Lab eBook that explores the work, ideas, and experiences of seven people whose work is revolutionizing data science and business, and having an impact on some of the world’s biggest problems. The eBook profiles:
What You Can Learn from These 7 Top InnovatorsIn our interviews, we asked these six data scientists—and a medical doctor doing groundbreaking work in federated learning and medical AI—about everything from the rise of data science, to where new breakthroughs might come from in the future, to career advice for aspiring innovators. Their answers are insightful, inspiring, and sometimes even surprising. All say that better tools for data scientists are a key driver of innovation. As Google’s Chief Decision Scientist, Cassie Kozyrkov, puts it: “The world is finally on board with understanding that ‘knowledge is power’ belongs in the same bucket of ideas as ‘data is useful.’” "And if data is useful, then data scientists are useful, and their work needs to be streamlined to extract value and accelerate innovation,” says Kozyrkov. Meet Cassie and other top leaders who are advancing the profession, achieving new value for their companies, and helping to solve the world's biggest problems in our book today. You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities. |
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😴 ❌ Don’t Sleep on JAX
Sunday, August 14, 2022
Weekly news digest curated by the industry insiders
📌 Event: Last chance to register for conference on scalable AI – Aug 23-24 in San Francisco!
Friday, August 12, 2022
The world's top minds in AI and distributed computing are coming to Ray Summit — August 23-24 in San Francisco. Join the global Ray community for two days of keynotes, training, and technical
🐈⬛ Edge#216: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different Domains
Friday, August 12, 2022
Gato is able to master tasks such as image classification, question answering or controlling a robotic arm
🏛 Edge#215: Pre-Train Model Testing and the Pillars of Robust ML
Tuesday, August 9, 2022
In this issue: we discuss Pre-Train Model Testing; we overview the pillars of robust machine learning; we explore Great Expectations
🏷 Data Labeling for ML: Survey
Monday, August 8, 2022
About 45% of the time in data science projects is consumed by processing and labeling data. It's fair to say that data labeling is one of the most expensive tasks of any machine learning project.
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