I’ve been in the shoes of the Reddit user who’s unsure about whether to focus on Large Language Models (LLMs) or dive into Machine Learning (ML). With experience in orchestrating agentic workflows and autonomous agents, they’re wondering if they should learn ML and ML ops to gain more control and reliability in production environments.
I totally get it. When you’ve built systems using customized prompts, it can be frustrating to have limited control over the outcome. And with new ML models emerging left and right, it’s natural to feel overwhelmed.
In my opinion, having a rudimentary knowledge of ML from a CS degree is a great starting point. But to really make a mark in the industry, it’s essential to deepen your understanding of ML and its applications. ML ops, in particular, can help you streamline your workflow and make your models more efficient.
So, should you learn ML or focus on LLMs? Well, it depends on your goals and priorities. If you want to create more sophisticated AI systems that can learn from data and improve over time, ML might be the way to go. But if you’re interested in fine-tuning language models for specific tasks or industries, LLMs could be the better choice.
Ultimately, the key is to stay curious, keep learning, and be open to new opportunities. The AI landscape is constantly evolving, and being adaptable is crucial to staying ahead of the curve.
What do you think? Are you facing a similar dilemma in your career? Share your thoughts in the comments!