Wednesday, February 19, 2025

I started this blog with the intentions laid out in my first post. However, getting started felt daunting. Months after writing that first post, I was listening to an interview with Daliana Liu on Super Data Science when she said something along the lines of, “If you’re one of those people with drafts lined up but are too afraid to publish, you should just do it.” I felt like she was talking directly to me. Her message was clear: the most important thing is to start. It’s okay to put out an imperfect product and improve over time. Another point that stuck with me was to write with intention—but that intention doesn’t have to be perfection or gaining thousands of followers. Writing is a way to process thoughts, reinforce learning, and build clarity. I’ve spent too many years not creating because I felt inadequate in a world already saturated with content.

With that in mind, I’ve decided to use this blog as a learning log, at least for now. Managing the knowledge, skills, and concepts I’ve been absorbing has been overwhelming. It often feels like I’m learning a lot yet not making any significant progress. Writing it down will help reinforce my learning and allow me to see tangible growth. I rarely recognize my own progress unless someone points it out or I force myself to take a step back and give myself credit. Another benefit is simply getting comfortable with taking up space online—imperfectly.

To be honest, I have over a dozen tabs open right now. I just finished a long-winded conversation with ChatGPT about my skills, progress, and next steps. I know I need to update my resume. I installed FastAPI, only to immediately switch directions and start learning Apache Airflow instead. My mind felt so cluttered that I finally sat down to write this—to get clarity before moving forward.

Another approach I’ve taken is keeping a real notebook with grid paper to jot down my thoughts, ideas, questions, and project progress. Since starting this, I’ve been able to capture so much more of what’s going on in my mind, helping me revisit essential problems and stay on track. I might expand on this in a future blog post.

But today, I want to set a foundation for my learning log by reflecting on my journey and laying out my roadmap—however dynamic it may be.

I spent years working toward a Master’s in Statistics, expecting to become a statistician or researcher. After a few failed interviews, I landed a role as a Coding Data Labeling Analyst at Meta. I found it hilarious that they hired me for a job requiring my weaknesses rather than my strengths. However, I quickly realized there was no good reason for coding and engineering to be a weakness. I became obsessed, and suddenly, the possibilities expanded.

An unexpected opportunity to interview for a DevOps/Data role lit a fire under me, and I started learning fast. Though I didn’t ultimately get the job, the experience gave me a much clearer understanding of what I like, what I know, and what I need to learn. Before that, I never thought I could be the kind of person who participated in coding interviews or aimed for engineering roles. I never even thought DevOps or data engineering would interest me. I was so wrong.

Preparing for that interview meant hours of watching YouTube videos on data structures and algorithms, reading Ace the Data Science Interview and Cracking the Coding Interview, and completing courses on CI/CD and testing. By the time I was done, the outcome of the interview didn’t even matter as much—I wanted the job, but I also walked away knowing I had leveled up. If I could do that, I could do more.

Now, I want to join the innovative AI and MLE engineers shaping this incredible era of AI potential. To do that, I feel like I need to learn everything—and that excites me.

Every day, I log off work and dive into my ever-changing, massively ambitious, self-made ‘curriculum,’ assisted by ChatGPT. My decisions are shaped by a mix of passion, interest, and the need to build essential skills. I’m also working on a challenging computer vision and robotics project (which I’ll probably write about in another post). There are skills I need now for roles I can realistically reach and skills I need to start learning for the roles I see myself growing into.

To get us up to speed, this was my most recent message to ChatGPT:

“I think I might not be focusing my immediate skill progression correctly. I’m advancing Python skills, completing the Deep Learning Specialization, reading Fluent Python, completing Leetcode DSA, and reading Reinforcement Learning and papers in the LLM, RL, and Agents space.”

I left out that I’m also working on a robotics project, leading a local AI book club, and reading Chip Huyen’s AI Engineering.

I can’t even keep track of everything I’m doing, so it’s time to refocus again.

I’m reading:

  • Reinforcement Learning: An Introduction – Because RL fascinates me, and understanding its theory will help me work on practical implementations in AI.
  • Fluent Python – Because I need to level up my Python programming skills to write cleaner, more efficient code.
  • AI Engineering – Because it offers an eye-opening perspective on AI deployment and the challenges beyond model training.
  • Research papers on LLMs, Agents, and RL – Because I want to understand the research trajectory and anticipate where the field is heading.
  • Life 3.0 by Max Tegmark – Because my AI Book Club picked it for the month, and it adds a philosophical dimension to AI discussions.

I’m taking:

  • Deep Learning Specialization (Coursera, DeepLearning.ai) – Because mastering deep learning concepts is foundational to my career in AI.
  • Machine Learning in Production – Because understanding how to deploy and scale AI models is just as critical as training them.

I’m building:

  • A robot that detects and picks up tennis balls autonomously – Because it’s a fun challenge, and collaborating with mechanical engineers pushes my practical programming skills.

I’m practicing:

  • Leetcode problems – Because strong algorithmic skills will prepare me for technical interviews.
  • Python exercises generated by ChatGPT – To reinforce my understanding of advanced Python concepts from Fluent Python.
  • Git and GitHub – Because version control and collaboration are fundamental for working in AI teams.
  • Docker and WSL – Because containerization and system environments are essential for ML and deployment.

And somehow, it still doesn’t feel like enough.

Additionally, I need to start building skills in ML production and data engineering.

I’ll start with:

  • Apache Airflow – To gain experience in workflow automation and data pipelines.
  • GitHub Actions – To automate software workflows and CI/CD.
  • Data Pipelines (maybe on AWS) – To work on scalable data infrastructure for AI applications.

Then eventually:

  • LLM Engineering – To understand and contribute to the next wave of AI advancements.
  • FastAPI – To build high-performance APIs for deploying AI models.
  • Agentic AI – To explore autonomous AI systems and decision-making models.

Every time I write out a plan, I feel an immediate sense of comfort. If I just follow it, execute, and refine it occasionally, I’ll inch closer and closer to my goals. And for now, that’s enough for me.