On this episode of The Artists of Data Science, we get a chance to hear from Carl Osipov, who has nearly two decades of information technology experience spanning roles such as program manager at Google, an IT executive at IBM, and as an adviser to Fortune 500 companies. Today, he's here to talk about his book, “Serverless Machine Learning In Action”, which is targeted at teams and individuals who are interested in building machine learning system implementations efficiently at scale.
Carl shares with us his take on the future impacts of machine learning, the creative process in feature engineering, and important soft skills that data scientists need to develop. Carl’s expertise and advice will resonate with beginners and senior data scientists alike. It was a great pleasure speaking with him!
WHAT YOU'LL LEARN
[5:01] Hype in machine learning and how it’s changed
[8:58] The potential negative impacts of machine learning
[38:21] Is machine learning an art or science?
[51:47] Important soft skills you need to succeed
[54:23] Tips on communicating with executives
QUOTES
[12:00] “I think what will make data scientists of tomorrow successful is going to be more about the understanding of human culture.”
[58:03] “The most important lesson is to be persistent and continue focusing on that one successful outcome. You only need to be successful once, so don't worry about any of those individual failures.”
[58:50] “Whenever you collaborate with someone and you're willing to learn from them, you're going to come away as a person who really grows as an individual…”
SHOW NOTES
[00:01:33] Introduction for our guest today
[00:03:03] What drew you to this field? What were some of the challenges you faced breaking into the field?
[00:04:46] How much more hyped has machine learning become since you first kind of broke into this?
[00:05:59] Where do you see now the field of machine learning headed in the next two to five years?
[00:07:41] What do you see being the biggest positive impact coming from machine learning in the next two to five years?
[00:08:52] What do you think would be the scariest application of machine learning in the next two to five years?
[00:10:55] What are some things that we should keep on top of our mind as areas of concern so that we can kind of mitigate the risk of these scary applications?
[00:11:45] What do you think will separate the great Data scientists from just the good ones?
[00:13:48] What is serverless machine learning and how is it different from regular old fashioned machine learning?
[00:17:10] So what is the difference between machine learning code and machine learning platform?
[00:19:14] What is it about the contemporary practice of machine learning that tends to just suck our productivity from the practitioner?
[00:21:24] At what point then does it make sense for us to start using serverless machine learning?
[00:23:05] The difference between row-oriented and column-oriented storage.
[00:27:21] A hypothetical scenario where serverless machine learning would an ideal use case.
[00:28:52] What tips you can share with our audience so that we can be more thoughtful with our feature engineering.
[00:31:55] What are some tips that you can share with our audience so that we can be more thoughtful in our hyperparameter tuning?
[00:34:17] What do we do once a model is put into production?
[00:38:07] Is data science an art? Or is it purely a science?
[00:39:51] The creative process in data science
[00:43:19] The democratization of machine learning
[00:45:21] What would you say was the biggest lesson you learned about democratization of A.I. while you're over at Google?
[00:46:16] We discuss the many patents Carl has published
[00:48:53] Which of your publications, your patents do you think are most applicable to our current times?
[00:51:24] What soft-skills do you need to be successful?
[00:53:49] How to communicate with executives
[00:55:54] How to develop your product sense and business acumen
[00:57:10]