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MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

FromMLOps.community


MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

FromMLOps.community

ratings:
Length:
60 minutes
Released:
May 21, 2020
Format:
Podcast episode

Description

MLOps meetup #12 // What are the advantages for a data scientist to know data engineering?   
What good is learning Data Engineering skills? These days full stack is overflowing with all the different things you need to know about so why learn data Engineering now? Our guest on this meetup will make the case for what the advantages are if you do decide to learn data engineering and also go into depth on how to do data engineering in the cloud.  
Dan Sullivan is a software architect and data scientist with extensive experience in big data, machine learning, data architecture, security, stream processing, and cloud architecture. Dan is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer exam guides as well as NoSQL for Mere Mortals.  
He is also the author of over ten LinkedIn Learning courses on data science, machine learning, SQL, data architecture, and NoSQL. He holds a Ph.D. in genetics, bioinformatics, and computational biology.  
Get a copy of his new book here: https://www.wiley.com/en-us/Official+Google+Cloud+Certified+Professional+Data+Engineer+Study+Guide-p-9781119618454  
Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw
Follow us on twitter:@mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g  
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Dan Sullivan on LinkedIn: https://www.linkedin.com/in/dansullivanpdx/
Released:
May 21, 2020
Format:
Podcast episode

Titles in the series (100)

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.