The first part of a loquacious guide to resources, tips, & lessons learned interviewing for Machine Learning Engineer roles. My goals for this blog series are to demystify the interview process for Machine Learning Engineer roles and avail readers of my personal experiences as a useful guide post. Over a…

Tl:dr 💁‍️ ➡️ 👩‍🔬; How I went from working the front desk of a hair salon to working as a data scientist without a Master’s, PhD, or quantitative college degree. A few words before diving in… Life comes at you fast. Since I published Part 3 (and started writing the first draft of Part 4), I’ve: taught at two data…

Tl:dr 💁‍️ ➡️ 👩‍🔬; How I went from hair salon girl to data scientist working for an innovative digital health company with no Master’s, PhD, or background in a quantitative college degree. “Work is love made visible.” — Kahlil Gibran “The written word is all that stands between memory and oblivion. Without books as…

Tl:dr 💁‍️ ➡️ 👩‍💻; How I went from hair salon girl to data scientist working for an innovative digital health company with no Master’s, PhD, or quantitative college degree. This section covers my attempts at hacking the learning process, choosing a boot camp, and building up my portfolio. Hi everyone! So I’m assuming you’re joining me…

We know MLOps and Production ML are hard. But what are the common challenges that plague teams and practitioners (regardless of industry or background) when it comes to deploying data science models to production? Sabrina Aquino from Shakudo organized a Twitter space called “ MLOps: Solving the Complexity of Moving…

📆 And What The Week Can Look Like Sec 1: Introduction — Who am I and why this blog post? Hey there! My name is Mikiko and (at the time of writing) I lead MLOps at Featureform.

A system of education is not one thing, nor does it have a single definite object, nor is it a mere matter of schools. Education is that whole system of human training within and without the school house walls, which molds and develops men. — W. E. B. Du Bois …

Life isn’t over, it just gets more interesting. #recoveringdatascientist Officially, I worked as a data scientist for a grand total of 2-ish years. Quite short but I learned a lot about myself and what I wanted for my professional career.

Career Capital is meant to be deployed & as a data scientist you have plenty of it. #recoveringdatascientist In Part 1 of this two-part series, we answered the following questions: ✅ Why Would Data Scientists Leave Data Science? (Or Even Data Entirely?); ✅ Some of the traits that make data…