Choosing and Fixing AI Models in Open Systems

Choosing and Fixing AI Models in Open Systems A Practical, Industry Ready PPT for Building Real World AI Systems This presentation was designed for a live talk at FOSSMeet’26 (NIT Calicut), focused on helping students and early professionals understand how AI models actually work in production and not just in theory.

What this PPT covers

1. Why AI models fail in real world systems

2. How to choose the right model based on real constraints

3. Practical frameworks: OPEN, CAFE, FIXED (experience driven approaches)

4. Understanding bias, variance, drift, and scalability issues

5. Common industry problems + how to fix them

6. A real world case study (NLP model failure & improvement)

7. Ethical AI and responsible deployment

8. Key insights from actual production systems.

Who is this for?

1. BTech students (all years) exploring AI/ML

2. Developers transitioning into Data Science / AI

3. Anyone tired of “just theory” and wants real world clarity

4. Participants preparing for Hackathons, Internships, ML interviews, Tech talks/presentations.

How one can use this:

1. As a learning roadmap for AI/ML fundamentals + real world concepts

2. As a reference guide while building projects

3. As a ready to use presentation (for college, workshops, or seminars)

If you're serious about moving from learning AI → applying AI, this PPT will give you that mindset shift.