If you want to work on AI systems in production, you’ll eventually need two kinds of skill. One is building AI applications: LLM apps, retrieval pipelines, agents. The other is operating them: versioning, automating, deploying, and monitoring models so they keep working after launch. AgileFever teaches these in two separate bootcamps, and people often ask about the MLOps bootcamp vs GenAI bootcamp choice: which one to take first.
The honest answer depends on what you do today. This post compares the two programs and suggests an order based on your current role.
Side by side
| MLOps BootCamp | GenAI and Agentic AI BootCamp | |
|---|---|---|
| Main question it answers | How do I run ML and LLM systems reliably in production? | How do I build LLM applications and AI agents? |
| Hours | 60, all live | 74 (64+ live, 10 self-paced) |
| Teaching weeks | 6 | 8 |
| Modules | 17 live | 17 live plus 1 recorded (n8n) |
| Capstone | Fraud detection or product recommendation, end to end on Google Cloud | 2 built live from 8 business scenarios, 6 more briefs on GitHub |
| Starting point | Basic Python. DevOps experience helps but isn’t required | Experienced IT professionals. Python for AI pre-work included |
| Assessment | No exam; modules plus capstone | No exam; capstone |
| Current price | $2,000 or ₹75,000 | $2,000 or ₹85,000 |
Additional discounts are available. Contact us to know more.
How much they overlap
Less than the names suggest. The MLOps BootCamp touches LLMs and agents, but only in its last two modules, LLMOps and Agentic AI Ops, where the focus is on deploying and monitoring them. The GenAI program has a production phase too, but going by its module titles, it doesn’t cover data versioning, experiment tracking, pipeline orchestration, or model monitoring with Prometheus and Grafana.
Number of live modules by focus area
Source: AgileFever curricula, October 2026. Grouping into focus areas is ours.
So if you take both, you’ll repeat very little. That’s also why the order matters: each one assumes different things about what you already know.
MLOps bootcamp vs GenAI bootcamp: which first, by role
| You are… | Start with | Why |
|---|---|---|
| A data scientist | MLOps | You already build models. The gap is getting them into production and keeping them there |
| A DevOps or platform engineer | MLOps | About a third of the syllabus will feel familiar, and the rest maps onto skills you already have |
| A cloud engineer | MLOps | Most of the program runs on cloud infrastructure you know, and it leads naturally to cloud ML roles |
| A software or backend engineer who wants to build AI features | GenAI and Agentic AI | You’ll ship something useful sooner, then learn to operate it later |
| An ML engineer | Whichever you know less about | If you’ve deployed models but never built an agent, take GenAI. If you’ve built prototypes that never shipped, take MLOps |
| Someone with only basic Python | MLOps | It starts from Python and ML foundations; the GenAI program expects more coding experience |
A simple test: think about the last AI project you worked on or wanted to work on. If it stalled because nobody could build it, start with GenAI. If it stalled because nobody could deploy or maintain it, start with MLOps.
If you plan to take both
Leave a gap. Take the first program, use what you learned at work for a few months, then take the second. You’ll get more from the second one with a real project in mind, and the cohorts run often enough that waiting costs little.
Taken together, the two programs come to 124 live hours. That’s a lot of evenings, so it’s worth being sure you need both. Many people find that one program plus on-the-job learning covers what their role actually asks for.
Upcoming start dates
| MLOps BootCamp | GenAI and Agentic AI BootCamp |
|---|---|
| October 17, 2026 | October 24, 2026 |
| November 16, 2026 | November 16, 2026 |
| December 12, 2026 | December 19, 2026 |
| – | January 11, 2027 |
US cohort dates from the program pages in October 2026; India cohorts sometimes start a day later. Check each program page for current dates.
Additional discounts are available. Contact us to know more.
FAQ
Can I take them at the same time?
You could, but it means 15 or more live hours a week. Most working professionals find one program at a time hard enough.
Is MLOps still worth learning now that LLMs are everywhere?
Yes. LLM applications still need versioning, evaluation, automated deployment, and monitoring. The MLOps program covers these for classical ML first and then applies them to LLMs and agents.
What to read next
Full details on each program: MLOps BootCamp: The Complete Guide and GenAI and Agentic AI BootCamp: The Complete Guide. For background on the two disciplines, see LLMOps vs MLOps.
