Start Here👇🏻
Hey folks! Welcome to my substack! 👋🏻
It’s possible that you found my substack through one of my other channels or platforms like Medium, Youtube, Twitter, or Github but if not, be sure to check them out!
My goal is to demystify MLOps & show how to develop high-quality ML products from scratch from a vendor & architecture agnostic perspective i.e. the stuff I wish I had when I was trying to break into ML Ops or Data Science.
I love:
✅ Developing scalable, resilient, & reliable production ML systems for companies like Mailchimp;
✅ Creating books, videos, courses, & workshops to teach MLOps best practices & architectures;
✅ Contributing thought leadership around applied ML through organizations like Nvidia;
✅ Hacking projects (both open-source & personal) using best-of-breed tools & practices from software engineering, DevOps, data engineering, & machine learning using Python, SQL, Solidity, GCP, Docker, Kubernetes, etc.
If you love those same things and want a better class of educational content, be sure to sign up now so you don’t miss the first issue!
In the meantime, tell your friends!
Want a preview? Stuff I’ve written so far includes:
Break into {X}
Data Science:
➡️ ✂️Breaking into Data Science- From Hair Salon to Data Scientist (Ch.1)🔍
➡️ 💈 Breaking into Data Science — Upskilling & Bootcamps (Ch.2)- 📊
➡️ ✂️ Breaking into Data Science — Applying & Interviewing (Ch.3)- 🔍
➡️ ✂️Breaking into Data Science — Getting the DS Offer & Next Steps (Ch.4)🔍
ML Engineering:
➡️ 👩🏻💻 Miki’s 🔥Hot-Takes🔥 on MLE Interviews: Types of Roles & Interview Prep (Part 1/2)
➡️ 👩🏻💻 Miki’s 🔥Hot-Takes🔥 on MLE Interviews: Top Advice & Resources (Part 2/2)
Effective Software Engineering for Data Science
➡️ Understanding the “Why” of VM’s, Containers, & Virtual Environments