How I Got to MLOps (Why Data Scientists Need MLOps, Part 1)
A few weeks ago, I had posted that I was leaving Intuit Mailchimp. But I didn’t say what was next.
And there was some chatter about whether I was leaving to go start my own company, go into content full-time, or even work as a VC!
To make a long story short, I’m excited to partner with the Featureform team to grow & develop a key player in the MLOps ecosystem.
Below I share a sneak peak about my motivations and hopes for the future of MLOps, as well as why I think Featureform is important for the stack of the future.
For the full details, please check out: https://bit.ly/3F7BSXq
Who am I
Howdy folks! My name is Mikiko Bazeley.
Some of you might recognize me from LinkedIn or Youtube and already know my story but for those who are new, here are the main bullet points:
- Before I joined Featureform, I worked as a Senior Engineer on the MLOps team at Mailchimp (which was then acquired by Intuit);
- I’ve worked for as a data analyst, data scientist, and MLOps engineer in my 7+ year career as well as teaching at bootcamps;
- I’m based in San Francisco, where I was born and raised.
One of the most common questions I get every time I’ve pivoted in my career is “How did you know you wanted to build a career in X?”.
The most recent variation has been “How did you and others working in production ML know you wanted to become MLOps Engineers?” .
How I Got To MLOps
Success is stumbling from failure to failure with no loss of enthusiasm– Winston Churchill
Before I started working on production machine learning as an MLOps Engineer, I was a struggling data scientist.
And before I was a struggling data scientist I was an overwhelmed analyst.
And even before that, I was a completely confused and lost growth hacker.
As an undergrad I initially attended UCSD with a rather vague idea of eventually going to medical school, taking classes in public health for fun while taking organic chemistry. However, I decided I wanted to understand humans at a more macro scale, like how we make decisions and codify practices into culture and spent my remaining undergraduate years studying biological anthropology and microeconomics.
Without realizing it, I would be engaging the practice of ethnography (studied during my time as an anthropology student) through intensive hands-on experiential learning as a data and machine learning practitioner. Every stage of my career followed a pattern of encountering a new environment, observing the pain-points of users in that environment, attempting to solve the pain-point, realizing the obvious solution was incomplete, thereby triggering inquiry into solving the “solution”.
After graduation I moved back to San Francisco to find a job where I could save a bit of money while figuring out my next steps. While working as a receptionist at a small hair salon I started to understand the challenges small-medium sized businesses faced, especially when it came to incorporating new technologies that would supposedly increase their revenue, such as CRMs and Point-of-Sale systems. I started to become curious about the power of leveraging data to help grow cash-strapped brick-and-mortar businesses.
The next chapter of my career after the salon was an introduction into DataTM, big and small, structured and unstructured. I would go on to work as a data analyst for an anti-piracy company, as a financial analyst at the largest residential solar companies in the US (working on supply chain forecasting & sales modeling), and then working as a hybrid data analyst/data scientist for the customer success team focused on BIM 360.
These companies were as different as could be (size, industry and data maturity) and yet I ran into similar categories of problems, like data access, navigating tribal knowledge and metadata about the data, and ensuring the insights and strategic recommendations I provided my key stakeholders (many of whom were the CXO’s & VP’s of their respective companies & organizations) were timely, consistent, & trustworthy.
Many of these challenges were magnified in the next chapter of my career, as a data scientist and then MLOps engineer. In growth and analytics, my primary responsibilities were to use data to enable visibility into the health of the business & assist in decision making, as well as provide diagnosis when needed.
As a data scientist I was now responsible for developing external-facing, predictive models and answerable to many key stakeholders when code broke. Instead of data flowing in a single direction (from source through transformation to consumer) data needed to flow in multiple directions, like a linked daisy chain of multi-armed Lovecraftian horror monsters.
Some of the biggest challenges I faced as a data scientist working at a digital adoption SaaS platform and a health devices company included the lack of engineering support, the difficulty in setting up and stitching tooling together, and coordinating the many moving pieces of a machine learning pipeline as a data scientist on an island.
Deploying models is hard, especially when you don’t know what “good” looks like.
It wasn’t until I joined Mailchimp as an MLOps Engineer that I began to see my experiences and hard-earned battle scars working on data and machine learning systems coming together as a career in and of itself. After joining Mailchimp and having the opportunity to be a part of a functional and effective organization successfully deploying machine learning features, I was inspired to dive even deeper into the world of MLOps.
For more about my Data Science & ML meanderings:
You can also find me on
- Youtube: https://bit.ly/3MBR8N3
- Twitch: https://bit.ly/3Akmwfe
- LinkedIn: https://linkedin.com/in/mikikobazeley/…
- Substack: https://mikikobazeley.substack.com
- Medium: https://bit.ly/3wKUwym
- Github: https://github.com/MMBazel
