Most machine learning models never become a dependable part of a product. The model often isn’t the problem. What’s usually missing is the work around it: versioning the data, retraining automatically, packaging the model, watching it once it’s live. That work is what MLOps covers, and it’s what the MLOps BootCamp teaches.
This guide covers what’s in the program, how the 60 hours are spent, what it costs, and who should pick something else.
The MLOps bootcamp at a glance
| Format | 100% live online, no self-paced component |
| Live hours | 60 |
| Modules | 17 |
| Teaching weeks | 6 (recent cohorts run across 7 to 8 calendar weeks) |
| Projects | 15+ hands-on projects across 11+ tools |
| Capstone | Fraud Detection or Product Recommendation system, deployed on Google Cloud |
| Assessment | No exam. Certificate on completing all modules and the capstone |
| Prerequisite | Basic Python. DevOps experience helps but isn’t required |
| Current price | $2,000 or ₹75,000 |
Additional discounts are available. Contact us to know more.
How the 17 modules are organized
The syllabus moves in four phases. It starts with Python and ML foundations so everyone is working from the same base, then spends the most time on the versioning and automation tools that make up day-to-day MLOps work.
Number of modules in each phase of the MLOps BootCamp
Source: AgileFever MLOps BootCamp curriculum, October 2026. Grouping into phases is ours.
Here’s what each phase includes and what you come away able to do.
| Phase | Modules | What you can do afterward |
|---|---|---|
| Foundations | Python, Python libraries for data science, statistics, supervised and unsupervised ML | Train and evaluate a model you’ll carry through the rest of the program |
| Versioning and automation | Deployment with VS Code, GitHub, and Streamlit; Git; CI/CD with GitHub Actions; MLflow; DVC; DagsHub | Track experiments, version data and models, and retrain and redeploy on every push |
| Production infrastructure | Docker; Airflow; Kubernetes; Grafana and Prometheus; AWS, Azure, GCP, and Vertex AI | Containerize a model, schedule pipelines, run it on a cluster, and monitor it in the cloud |
| LLMOps and Agentic AI Ops | LLMOps; Agentic AI Ops | Apply the same discipline to LLM applications and agents, where outputs are harder to test |
A note on the LLMOps and Agentic AI Ops part
The full name of the program mentions LLMOps and Agentic AI Ops, and both are covered, but as two modules at the end. That’s about 12% of the syllabus. Most of the program is classical MLOps, which is still where most production ML jobs sit.
If you’re mainly interested in operating LLM apps and agents, this ordering still makes sense: prompt versioning, evaluation pipelines, and agent monitoring all build on the experiment tracking, CI/CD, and observability you learn earlier. Just don’t expect a deep LLMOps program. If building agents is your main goal, the GenAI and Agentic AI BootCamp is the closer fit.
The capstone
You choose one of two capstones: a fraud detection system or a product recommendation system. Either way, you build it as a full pipeline on Google Cloud, with data versioning, automated training, containerized deployment, and monitoring. That’s the piece worth putting at the top of your GitHub, because it shows the end-to-end work hiring managers ask about in MLOps interviews.
Who it’s for
The program lists these as the most common moves people make after it:
| Your current role | Role it prepares you for |
|---|---|
| Data scientist | MLOps engineer |
| DevOps engineer | ML platform engineer |
| Software engineer | ML Ops engineer |
| Backend developer | LLMOps engineer |
| Cloud engineer | Cloud ML engineer |
| ML engineer | AI infrastructure engineer |
| IT professional | AI/ML DevOps engineer |
Data scientists and DevOps engineers tend to get the most from it, for opposite reasons. Data scientists already know models and are missing the deployment and infrastructure half. DevOps engineers already know containers and pipelines and are missing the ML half. The program covers both, so each group will find one stretch easy and the other hard.
Who should choose something else
- If you’ve never written Python, you’ll struggle. Start with Python for AI
- If you already run Kubernetes and MLflow in production, much of the program will be review
- If you want to build LLM apps and agents rather than operate ML systems, look at the GenAI and Agentic AI BootCamp
- If you need a vendor certification such as AWS Machine Learning Specialty, this program doesn’t prepare you for that exam
Cost
| United States | India | |
|---|---|---|
| Price | $2,000 | ₹75,000 |
| Cost per live hour | about $33 | about ₹1,250 |
| EMI | From $83/month at 0% interest | Check with the team |
The price includes the live sessions, session recordings, projects, the capstone, and career support: resume, LinkedIn, and GitHub reviews plus mock interviews. Cloud usage for your capstone may cost a little extra if you go beyond free-tier limits, so set a billing alert on day one.
Upcoming cohorts
| Starts | Ends |
|---|---|
| October 17, 2026 | December 5, 2026 |
| November 16, 2026 | January 11, 2027 |
| December 12, 2026 | January 30, 2027 |
Dates are from the program page in early October 2026 and can change. Check the MLOps BootCamp page for the current schedule and time zones.
Additional discounts are available. Contact us to know more.
FAQ
Is there an exam?
No. You get the certificate after completing all modules and the capstone.
Can I watch recordings if I miss a session?
Yes. Live sessions are recorded, so you can catch up on anything you miss. The labs still go better when you attend live and can ask questions as you work.
Do I need DevOps experience?
No, but it helps. If you’ve never used Git or a terminal, spend a weekend on both before the program starts.
Which cloud do we use?
The cloud module covers AWS, Azure, GCP, and Vertex AI. The capstone is built on Google Cloud.
What to read next
For salary ranges and the skills employers ask for, read the MLOps career roadmap: skills and salary. If you’re weighing a bootcamp against self-study, see AI courses vs bootcamps: which one to choose.
