Beginner’s Guide to AI: Diving Into My First AI Blog Post

Hi there! I’m Hitesh Pattanayak, and I currently work in the engineering vertical at InfraCloud. As of this moment, you’re reading the very blog I’m writing. So meta, right?

The Start of Something LLM-azing

When I first dipped my toes into the vast ocean of Large Language Models (LLMs), I couldn’t help but wonder:

  • What are these mystical “LLMs” everyone keeps talking about?
  • How do they work? Do they have a secret society I need to join?
  • How in the world do they get trained? Is there a gym for models? 🏋️‍♂️
  • And what exactly is “fine-tuning”? Is it like tuning a guitar, but with data?

I had all these questions swirling around in my head. The problem? I had no idea where to start. Every time I searched for resources, I felt like I was playing a game of “information scavenger hunt” – bits and pieces scattered all over the internet. Oh, and did I mention, I was looking for free resources?

Not that I’m cheap, especially when it comes to learning! It’s just… why spend money on something when I’m not even sure it’s useful yet? For example, if I want to master Kubernetes, I’ll head straight to Kodekloud – it’s a reliable platform. But for AI? It was a whole new ballgame.

The Reality Check: Embracing the AI Learning Curve

I quickly realized that if I waited for the perfect, neatly structured resource to fall into my lap, I’d be old and grey before I even got started. (And while I love a good grey hair, I’d prefer not to wait that long.) So, I figured it’s now or never. Dive in, swim around, and try not to drown in the sea of AI, GenAI, LLMs, and RAGs.

And hey, while I’m at it, why not blog about it? Writing helps me process and retain information. Plus, if I can make sense of this, maybe I can help others too!

Learning Isn’t Linear: My Approach to AI

Spoiler alert: learning is messy – especially when you’re tackling a new subject. (New for me, at least. Some of my colleagues seem to have been born with a neural net installed in their brains.)

So, how do I start? It helps that in my organization, there are some folks grinding away on cutting-edge AI stuff. These people are so deep into AI, they probably dream in machine code. While I find it hard to keep up with them, there’s a silver lining: they’ve put together a handy list of resources for beginners like me. Thank you, geniuses!

The AI Learning Path: Choose Your Adventure

The resources are split into three main categories based on API use cases:

  1. AI Ops – If you want to make machines manage machines. Because why not?
  2. AI Cloud – The magic of AI, but make it float on a cloud. ☁️
  3. AI Apps – Cool AI-powered applications. Because, let’s face it, we all love apps.

To ensure you don’t get in over your head too quickly, each category is further divided into three difficulty levels:

  • Crawl – Baby steps. You won’t break a sweat. 🍼
  • Walk – A leisurely stroll. Maybe bring some snacks. 🍿
  • Run – Full sprint. You might want to stretch first. 🏃‍♂️

So, the game plan is simple: pick a category (I’m going with AI Apps), start at Crawl, and level up from there. Easy enough, right?

My AI/ML Experience (or Lack Thereof)

Let’s take a brief stroll down memory lane, shall we?

  1. In one of my previous organizations, I took a course on creating classifiers and regression models. We dabbled with cool algorithms like Random Forest and played with datasets from Kaggle. We used Python and Google Colab notebooks to work through everything. Sounds impressive, right? Well, here’s the kicker: I don’t remember any of it. Like, none. Because if you don’t use it, you lose it.

  2. At another company, I was part of a hackathon team that built a chatbot using the RASA library in Python. Spoiler alert: we won the hackathon! 🎉 But guess what? I didn’t retain that info either. Seems to be a pattern with me, huh?

So, basically, I’m starting from scratch. 🫠 But hey, that’s okay! This time, I’m going to take a top-down approach to learning AI. Why? Because starting from the bottom just feels like too much work.

Stay Tuned for More AI Insights

So that’s where I am right now – at the beginning of a new AI journey. Stick around, and I’ll share everything I learn along the way. Let’s see if I can retain it this time… fingers crossed! 🤞

Until next time!

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