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What data scientists need to know about MLOps principles, from GPA 2.6 to Sr. MLOps Engineer@Intuit - Mikiko Bazeley - the data scientist show051
Mikiko Bazeley is a senior software engineer working on MLOps at Intuit. Previously, she worked as a growth hacker, data analyst in Finance, then become a data scientist, and later transitioned into machine learning. She has a bachelor degree in econ, biological anthropologie, did data science bootcamp at springboard. She is a tech writer for NVIDIA and she’s working on a course on MLOps. Her goal is to demystify MLOps & show how to develop high-quality ML products from scratch. You can find her content on Linkedin and YouTube. Today, we’ll talk about useful engineering principles for data scientists, MLOps, and her career journey. Subscribe to www.dalianaliu.com for more on data science and career. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science.
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/
Daliana's Twitter: https://twitter.com/DalianaLiu
Mikiko's Linkedin: https://www.linkedin.com/in/mikikobazeley/
Highlights:
(0:00) Intro
(00:02:00) from GPA2.6 to data scientist
(00:05:27) her experience at Mailchimp
(00:11:44) her frustrations on Cookiecutter project
(00:14:09) the pain point of a data scientist working with engineering
(00:21:01) 2 MLOps pattern
(00:25:52) challenges about her work
(00:29:49) the basic engineering skills a data scientist should have
(00:32:46) the tests a data scientist should write
(00:37:42) how an MLOps engineer collaborates with a data scientist
(00:45:28) what makes a good MLOps engineer
(00:52:33) AWS vs GCP vs Azure
(00:58:59) how a data scientist collaborates with an MLOps engineer
(01:05:19) suggestions for building a model on a large scale
(01:09:11) how she learnt MLOps on her own within 6 months
(01:17:32) learn from code review
(01:19:17) MLOps books and resources she recommended
(01:24:13) mistakes she made earlier in her career
(01:31:29) common mistakes people make during career change
(01:38:22) "Start with the end in mind"
(01:41:16) the future of MLOps
(01:46:23) how she sees her career growth
(01:56:40) how she continues learning new skills
(02:00:09) what she is excited about her career and life
View all episodes
By Daliana Liu
4.7
7575 ratings
Mikiko Bazeley is a senior software engineer working on MLOps at Intuit. Previously, she worked as a growth hacker, data analyst in Finance, then become a data scientist, and later transitioned into machine learning. She has a bachelor degree in econ, biological anthropologie, did data science bootcamp at springboard. She is a tech writer for NVIDIA and she’s working on a course on MLOps. Her goal is to demystify MLOps & show how to develop high-quality ML products from scratch. You can find her content on Linkedin and YouTube. Today, we’ll talk about useful engineering principles for data scientists, MLOps, and her career journey. Subscribe to www.dalianaliu.com for more on data science and career. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science.
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/
Daliana's Twitter: https://twitter.com/DalianaLiu
Mikiko's Linkedin: https://www.linkedin.com/in/mikikobazeley/
Highlights:
(0:00) Intro
(00:02:00) from GPA2.6 to data scientist
(00:05:27) her experience at Mailchimp
(00:11:44) her frustrations on Cookiecutter project
(00:14:09) the pain point of a data scientist working with engineering
(00:21:01) 2 MLOps pattern
(00:25:52) challenges about her work
(00:29:49) the basic engineering skills a data scientist should have
(00:32:46) the tests a data scientist should write
(00:37:42) how an MLOps engineer collaborates with a data scientist
(00:45:28) what makes a good MLOps engineer
(00:52:33) AWS vs GCP vs Azure
(00:58:59) how a data scientist collaborates with an MLOps engineer
(01:05:19) suggestions for building a model on a large scale
(01:09:11) how she learnt MLOps on her own within 6 months
(01:17:32) learn from code review
(01:19:17) MLOps books and resources she recommended
(01:24:13) mistakes she made earlier in her career
(01:31:29) common mistakes people make during career change
(01:38:22) "Start with the end in mind"
(01:41:16) the future of MLOps
(01:46:23) how she sees her career growth
(01:56:40) how she continues learning new skills
(02:00:09) what she is excited about her career and life
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