MLOps – Machine Learning Operations
₹240,000
Total Fee₹20,000/month
EMI Starting
Why Choose This Program?
End-to-End Deployment: Build pipelines, deploy models, and scale ML workflows using real infrastructure and DevOps tools.
Cloud-Native Learning: Use AWS, GCP, and Azure to simulate real-world production MLOps environments.
Expert Mentorship: Weekly sessions, code reviews, and project feedback from industry professionals.
Placement Support: Mock interviews, resume reviews, and job referrals customised to MLOps and ML Engineering roles.
💰 Program Fee: ₹2,40,000 | EMI starts at ₹20,000/month
What Makes Us Unique?
| Features | Our Program | Traditional Courses | Free Tutorials |
| Real MLOps Projects | ✅ Yes | ❌ Rare | ❌ No |
| Cloud-Native Workflows | ✅ Yes | ❌ Minimal | ❌ No |
| Placement Support | ✅ Yes | ✅ Yes | ❌ No |
| Payment Flexibility | ✅ Yes | ❌ Rare | ✅ Yes (Limited) |
How This Program Works
1. Real-World Module Development
CI/CD pipelines for model training and deployment
Model packaging with Docker
Kubernetes-based orchestration
Monitoring with Prometheus and Grafana
2. Projects to Build Your Portfolio
Fraud detection pipeline with automated retraining
NLP model deployed via FastAPI
Recommendation engine with CI/CD
Real-time ML pipeline using cloud infrastructure
3. Continuous Feedback & Improvement
Weekly mentor check-ins and code reviews
Debugging support and architecture feedback
Performance tuning and best practices
Iterative refinement of projects and pipelines
4. Placement Preparation
Mock interviews for MLOps and DevOps roles
Resume building with project highlights
GitHub portfolio setup and review
Career guidance and hiring referrals
Who Is This Program For?
✅ Beginners in AI/ML – Add deployment and DevOps capabilities to your machine learning foundation.
✅ Working Developers – Move into ML DevOps or production AI roles with modern tools and workflows.
✅ Career Switchers – Build cloud-based MLOps skills and enter one of the fastest-growing fields in tech.
Tools & Technologies You’ll Master
Frontend / API Layer
- FastAPI
- Streamlit
Backend & Deployment
- Docker
- Kubernetes
- GitHub Actions
- Jenkins
Cloud Infrastructure
- AWS
- Google Cloud Platform (GCP)
- Microsoft Azure
Monitoring & Versioning
- MLflow
- DVC
- Prometheus
- Grafana
Program Timeline
Months 1–3: MLOps Foundations
Understand ML lifecycle, use Git, DVC, and MLflow for versioning, and set up reproducible experiment tracking.
Months 4–6: CI/CD & Model Deployment
Automate training and deployment using GitHub Actions, Jenkins, Docker, and serve ML models via FastAPI.
Months 7–9: Scaling with Kubernetes
Deploy models using Kubernetes clusters, monitor metrics, detect drift, and schedule retraining jobs.
Months 10–12: Capstone Projects & Job Prep
Build complete pipelines, deploy on cloud platforms, simulate real scenarios, and prepare for job interviews.
Career Outcomes
Get ready for roles like:
MLOps Engineer
ML DevOps Engineer
AI Infrastructure Specialist
Why MLOps?
High Demand: MLOps engineers are among the most sought-after roles in modern AI teams.
Scalable Solutions: Learn to build AI systems that can scale reliably in production.
Cross-Disciplinary Edge: Combine data science, DevOps, and cloud to future-proof your tech career.
Frequently Asked Questions (FAQs)
Apply Now
Ready to launch your career in MLOps and become a job-ready AI deployment expert?
Apply today and start building real-world pipelines that power modern machine learning.
👉 [Apply Now]
Contact & Pricing
💰 Total Fee: ₹2,40,000
📆 EMI: ₹20,000/month
📞 Call/WhatsApp: +91-8095858589
📧 Email: info@gullyacademy.com
Refund Policy
We offer a 100% refund if you withdraw within the first 2 weeks of the program—no questions asked.