I've had the same conversation in about a dozen college staff rooms over the past two years.
A TPO or HOD pulls up their current AI/ML course syllabus — often a PDF that hasn't been touched since 2020 — and asks me whether it prepares students for the industry. They already know the answer. They're asking because they want someone outside their department to say it out loud.
The answer is: not really. And here's why.
The Textbook Problem
Most AI/ML courses in engineering colleges are taught from textbooks published between 2015 and 2019. Those books cover:
- Logistic regression, decision trees, SVMs
- Basic neural network math (backpropagation, gradient descent)
- "Introduction to Deep Learning" chapters that stop at CNNs
- R or "MATLAB" exercises that no one uses in production
None of this is wrong. Some of it is genuinely important foundational knowledge. But it describes a version of AI that the industry left behind several years ago.
The gap between what's in those textbooks and what a fresh hire is expected to know on day one is enormous — and it's widening every year.
What the Industry Actually Uses
When I'm hiring or reviewing resumes at Cosmoverge, here's what I'm actually looking for:
For a junior ML role:
- Can you fine-tune a pretrained model on a custom dataset?
- Do you know how to use Hugging Face pipelines?
- Can you wrap a model in a FastAPI endpoint?
- Have you worked with vector databases for retrieval?
For a GenAI / LLM role:
- Can you build a RAG pipeline?
- Do you understand prompt engineering beyond "write better prompts"?
- Have you worked with LangChain or LlamaIndex?
- Do you know the difference between fine-tuning and in-context learning?
None of these appear in a 2019 textbook. Most of them didn't exist in 2019.
The Lab Problem
Even when colleges have updated theory content, the lab component lags even further. The typical lab exercise:
- Load the Iris dataset
- Train a random forest classifier
- Print accuracy
This builds zero transferable skills. In the real world, you spend 80% of your time on data — collecting it, cleaning it, augmenting it, versioning it. You don't get handed a pre-cleaned CSV.
A better lab exercise:
- Scrape or photograph 200 images from a real scenario
- Annotate them using Roboflow
- Train a YOLO model
- Evaluate with mAP
- Deploy as an API
That's one day of hands-on work that builds five tangible skills.
Why This Isn't the Faculty's Fault
Faculty are not failing their students because they're lazy or incompetent. They're failing because the system isn't designed for keeping pace with this field.
A faculty member teaching AI/ML is also teaching two other subjects, managing their own research, preparing for NAAC audits, and attending departmental meetings. Finding time to rebuild a course from scratch — using tools that changed fundamentally 18 months ago — is not realistic.
This is why FDPs matter. Not as tick-box exercises, but as actual skill transfer sessions where faculty get hands-on with the tools their students will encounter in interviews.
What a Better Approach Looks Like
When I design a workshop or FDP for a college, I start with a simple question: what would a first-year employee at a product company be expected to do on day 30?
Then I work backwards from there.
The answer in 2024 almost always involves:
- Working with pre-trained models, not building from scratch
- Using APIs (OpenAI, Hugging Face, Roboflow) as first-class tools
- Understanding deployment — Docker, APIs, basic cloud
- Knowing how to iterate on prompts and evaluate model outputs
The Opportunity for Colleges
Colleges that update their AI/ML programs are going to see measurable placement outcomes in the next 2–3 years. The companies hiring right now are specifically looking for students who have shipped something with GenAI.
A student who has done a 2-day hands-on workshop where they built and deployed something walks into an interview with a fundamentally different story to tell than one who trained a logistic regression model on the Iris dataset.
Harshith runs AI/ML workshops and FDPs for engineering colleges across India. If you're thinking about updating your college's AI curriculum or running a student workshop, reach out here.