Uploaded October 2022 | Updated September 2026, 2 weeks ago
Mikiko Bazeley is a senior software engineer working on MLOps at Intuit. Previously, she worked as a growth hacker, data analyst in Finance, then became a data scientist. Later she transitioned into machine learning. She has a bachelor degree in econ, biological anthropology. She did the 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 and show how to develop high-quality ML products from scratch. You can find her content on LinkedIn and YouTube. She builds cool stuff and shows other people how to build cool stuff with ML. We talked about useful engineering principles for data scientists, MLOps, and her career journey. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on dalianaliu.com for more on data science.
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Daliana's Twitter: twitter.com/DalianaLiu
Mikiko's LinkedIn: linkedin.com/in/mikikobazeley
Her podcast: superdatascience.com/podcast/mlops-machine-learning-operations
The MLOps Engineer w/ Mikiko Bazeley: https://mikikobazeley.notion.site/The-MLOps-Engineer-w-Mikiko-Bazeley-af09f5896d804ad8b904590796ffddb7
Resources & Books Mikiko recommended:
Full Stack Deep Learning: fullstackdeeplearning.com
Made With ML: madewithml.com
MLOps Community: https://mlops.community/
Machine Learning Systems Design: huyenchip.com/machine-learning-systems-design/toc.html
Reliable Machine Learning: Applying SRE Principles to ML in Production: amazon.com/Reliable-Machine-Learning-Principles-Production/dp/1098106229
Practical MLOps: Operationalizing Machine Learning Models: amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Pragmatic AI: An Introduction to Cloud-Based Machine Learning: amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860
The Missing Semester of Your CS Education: https://missing.csail.mit.edu/
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 the Cookiecutter project
00:14:09 the pain point of a data scientist working with engineering
00:21:01 two MLOps patterns
00:25:52 challenges about her work
00:29:49 basic engineering skills a data scientist should have
00:32:46 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 how to a model on a large scale
01:09:11 how she learnt MLOps on her own in 6 months
01:17:32 learn from code review
01:19:17 MLOps books and resources
01:24:13 mistakes she made
01:31:29 common mistakes in career transition
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
Mikiko Bazeley is a senior software engineer working on MLOps at Intuit. Previously, she worked as a growth hacker, data analyst in Finance, then became a data scientist. Later she transitioned into machine learning. She has a bachelor degree in econ, biological anthropology. She did the 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 and show how to develop high-quality ML products from scratch. You can find her content on LinkedIn and YouTube. She builds cool stuff and shows other people how to build cool stuff with ML. We talked about useful engineering principles for data scientists, MLOps, and her career journey. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on dalianaliu.com for more on data science.
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Daliana's Twitter: twitter.com/DalianaLiu
Mikiko's LinkedIn: linkedin.com/in/mikikobazeley
Her podcast: superdatascience.com/podcast/mlops-machine-learning-operations
The MLOps Engineer w/ Mikiko Bazeley: https://mikikobazeley.notion.site/The-MLOps-Engineer-w-Mikiko-Bazeley-af09f5896d804ad8b904590796ffddb7
Resources & Books Mikiko recommended:
Full Stack Deep Learning: fullstackdeeplearning.com
Made With ML: madewithml.com
MLOps Community: https://mlops.community/
Machine Learning Systems Design: huyenchip.com/machine-learning-systems-design/toc.html
Reliable Machine Learning: Applying SRE Principles to ML in Production: amazon.com/Reliable-Machine-Learning-Principles-Production/dp/1098106229
Practical MLOps: Operationalizing Machine Learning Models: amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Pragmatic AI: An Introduction to Cloud-Based Machine Learning: amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860
The Missing Semester of Your CS Education: https://missing.csail.mit.edu/
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 the Cookiecutter project
00:14:09 the pain point of a data scientist working with engineering
00:21:01 two MLOps patterns
00:25:52 challenges about her work
00:29:49 basic engineering skills a data scientist should have
00:32:46 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 how to a model on a large scale
01:09:11 how she learnt MLOps on her own in 6 months
01:17:32 learn from code review
01:19:17 MLOps books and resources
01:24:13 mistakes she made
01:31:29 common mistakes in career transition
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
