Future AI Skills: Machine Learning or Agentic AI — What’s in Higher Demand?

AI Discussions
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Hi everyone,

I’m currently certified in Deep Learning and Agentic AI through Deeplearning.ai, and I’ve been thinking about what to focus on next in my learning journey.

With AI evolving so quickly — from traditional machine learning and LLMs to the emergence of agentic AI systems — I’m curious about which skill sets will be most valuable in the near future.

I’d love to hear your perspectives:

  • Do you see more demand right now for core ML and model development, or for skills related to AI agents, orchestration, and tool integration?

  • For someone who already understands deep learning and agentic frameworks, what would be a smart next area to explore — e.g., RAG, MLOps, prompt engineering, AI safety, or multi-agent systems?

Thanks in advance — I’m really looking forward to hearing how others in this community are thinking about the next wave of high-impact AI skills!

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All of these.

Tip:
Learn everything you can now, this will set you up to be an efficient learner for the new technology that will emerge in the next six months.

It’s an extremely rapidly growing field. By the time you’re a master in current methods, your knowledge will be obsolete if you want to be on the leading edge.

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Hi
I have a question. Please How or where can i interact with people whore are coding rea time share their mistakes , we debug together. and so on like a group telegram

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Re: “debug together”

For certificate courses, DL.AI does not allow programming assignments to be a group effort. Each learner must do their own work.

For your other topics, you can use the DL.AI community discussion forums. Each course has it’s own topic area.

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Thanks

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Learning is great, but without applying what you learn, you’ll forget it—especially with new tools and frameworks popping up all the time. My take:

  • Build a broad foundation so you can navigate the AI landscape.

  • Then go deep on one focus area and aim to master it.

  • Turn knowledge into skill by shipping projects: small end-to-end builds, Kaggle comps, or micro-tools.

  • Contribute to open source (bug fixes, docs, small features) to learn real-world practices and get feedback.