Today marks the start of something I've been wanting to do for months - document my journey from a beginner in Machine Learning & MLOps to building and deploying real-world systems.
I'm still in college, which means my daily routine is a mix of:
Attending lectures (today was all math, math, and more math ).
Solving probability & statistics problems.
Translating those solutions into Python code.
Watching my experiments fail, then tweaking until they work (or until my coffee runs out).
Why I'm Starting This Series
I want to:
Share practical learnings from the intersection of math, code, and ML pipelines.
Show the real day-to-day grind - not just polished results.
Connect with developers, ML learners, and startup founders who might want to collaborate.
Today's Work
Task: Solve a set of statistical probability problems from my professor.
Approach:
1.Paper solution to understand the theory.
2.Wrote Python scripts to simulate the problem with different data sizes.
3.Compared simulated results vs theoretical predictions.
4.Documented findings for reproducibility (future me will thank me).
Even though it's "beginner" work, I believe good habits from day one make the difference when you reach production-level challenges.
What's Next
Learn more about versioning experiments.
Try basic MLOps tooling.
Share small but working code from my daily workflow.
💬 If you're in ML, MLOps, llm
- let's connect!
Especially if you're into reinforcement learning, meta-learning, or scaling AI experiments.
FOLLOW ME ON X :- SATYAM MISTARI (@SatyamMistari14) / X
Top comments (1)
Love how you’re showing the messy, real side of learning ML, not just the polished end results. This is exactly the kind of transparency that helps beginners stay motivated. 🚀 Keep sharing!