671: Cloud Machine Learning

Get to grips with AWS, Azure, Google Cloud Platform on this week’s episode. Host Jon Krohn speaks with Kirill Eremenko and Hadelin de Ponteves about CloudWolf, a cloud computing educational platform that prepares students for certification in AWS (Amazon Web Services). Find out why an accreditation in cloud computing could be the safest investment for your data science career. This episode is brought to you by Posit, the open-source data science company (https://posit.co/academy), and by AWS Inferentia (https://aws.amazon.com/ec2/instance-types/inf2/?trk=bbd10c3f-c200-4629-bca8-adf6ad324c9e&sc_channel=el). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • About CloudWolf [07:04] • Why learning the cloud is important for data scientists [09:12] • Is learning cloud computing complex? [22:30] • Essential AWS services [28:31] • Database options on AWS [33:47] • How to run analytics on AWS [40:58] • Why an AWS certification is so helpful [56:35] Additional materials: www.superdatascience.com/671

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.