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Best Cloud AI & MLOps Institute in Bhubaneswar
Cloud AI
& MLOpsDeploy & Scale AI Models on AWS, Azure & GCP — Bhubaneswar, Odisha
Eligibility: Graduation | Duration: 8 Months
Course Snapshot
Explore the key highlights of our Cloud AI & MLOps program including cloud deployment, AI infrastructure, MLOps systems, certification, and real-world cloud projects.
Course Duration
8 Months
Eligibility
Graduates, Developers & Cloud Enthusiasts
Learning Mode
Offline + Hybrid Learning
Certification
Industry-Recognized Certification
Projects & Capstone
Cloud Deployment Projects, MLOps Pipelines & AI Infrastructure Systems
AI Tools Covered
AWS, Azure, GCP, Kubernetes, Docker, MLflow & CI/CD Pipelines
AI Skills Covered
Cloud AI Deployment, MLOps, AI Infrastructure & Model Scaling
Career Support
Career Support + Interview Preparation
Internship
Internship Assistance Available
8 Months
Professional Program
100%
Placement Assured
Industry Certified
Recognised by Cloud & AI Firms
Why Choose Cloud AI & MLOps?
Models are useless if they can't scale. Learn to build the infrastructure that powers modern AI applications.
Multi-Cloud Mastery
Deploy on AWS SageMaker, Azure ML, and Google Vertex AI.
MLOps Best Practices
Master CI/CD, Model Versioning, and Monitoring.
Containerization
Docker, Kubernetes, and Kubeflow for scalable AI.
High Salaries
MLOps Engineers earn ₹11-20 LPA in top tech companies.
Expert Mentors
Learn from Cloud Architects and MLOps leads.
Real Infrastructure
Hands-on labs with live cloud accounts and clusters.
What You Will Learn from this course:
Deploy machine learning models on AWS SageMaker, Azure ML, and Google Cloud Vertex AI.
Build end-to-end ML pipelines with automated training and deployment workflows.
Implement model versioning and experiment tracking using MLflow and Weights & Biases.
Create containerized ML services using Docker and orchestrate with Kubernetes.
Monitor ML models in production and detect data drift and performance degradation.
Apply CI/CD best practices for machine learning with automated testing.
Optimize model serving for low latency and high throughput.
Work on production ML systems including recommendation engines and fraud detection.
Course Content & Software Skills
Career Opportunities After This Course
Explore high-demand roles in MLOps, cloud AI, and machine learning infrastructure.
MLOps Engineer
Build and maintain ML pipelines and infrastructure.
Cloud ML Engineer
Deploy scalable models on AWS/Azure/GCP.
ML Platform Engineer
Develop internal tools for data science teams.
DevOps Engineer (ML)
Automate software and ML delivery workflows.
Data Engineer (ML)
Build data pipelines for model training.
ML Infrastructure Architect
Design cloud architecture for AI workloads.
Production ML Engineer
Focus on model serving and optimization.
Site Reliability Engineer (ML)
Ensure uptime and reliability of AI systems.
Cloud Solutions Architect
Design end-to-end cloud AI solutions.
Principal MLOps Engineer
Lead MLOps strategy and implementation.
Admission & Program Details
Cloud AI is the backbone of modern tech. We offer scholarships to make advanced cloud computing training accessible.
Enrollment Guidelines
Cloud AI is the backbone of modern tech. We offer scholarships for engineering graduates and cloud enthusiasts to support their journey.
Required Documents
- Caste certificate (if applicable)
- Residential proof
- Aadhar card
- Bank passbook
- Graduation Marksheet
Merit-based and need-based scholarships up to 50% off on course fees.
Program Details
Frequently Asked
Questions
Explore answers to common questions about our Cloud AI & MLOps course, Machine Learning deployment, cloud automation, certification benefits, and future career opportunities in scalable AI infrastructure and operations.
The Cloud AI & MLOps course is an advanced program designed to help learners understand cloud-based Artificial Intelligence, Machine Learning operations, AI deployment workflows, automation pipelines, scalable AI infrastructure, and modern DevOps practices for AI-powered applications.
This course is ideal for software developers, AI engineers, data professionals, cloud engineers, DevOps professionals, students, working professionals, and technology enthusiasts who want to build expertise in cloud-based AI systems and Machine Learning deployment workflows.
Basic programming and computer knowledge are helpful for this course. Learners with backgrounds in software development, cloud computing, Artificial Intelligence, or data analysis will benefit the most, although beginner-friendly guidance is also provided for foundational concepts.
Students will learn cloud computing fundamentals, Machine Learning deployment, AI workflow automation, MLOps pipelines, model monitoring, scalable AI infrastructure, containerization concepts, cloud-based AI services, automation tools, and production-ready AI system management.
MLOps, or Machine Learning Operations, is the process of managing, deploying, monitoring, and automating Machine Learning models in production environments. It helps organizations improve scalability, reliability, efficiency, collaboration, and continuous delivery of AI-powered applications.
Students will gain practical exposure to cloud AI workflows, Machine Learning deployment environments, automation tools, AI pipelines, scalable infrastructure concepts, and modern cloud-based Artificial Intelligence development practices used in the industry.
Yes. Students will work on hands-on projects involving AI model deployment, workflow automation, cloud-based AI applications, Machine Learning pipelines, and real-world MLOps scenarios designed to build industry-ready skills.
After completing this course, students can pursue career opportunities such as Cloud AI Engineer, MLOps Engineer, AI Deployment Specialist, DevOps Engineer, Machine Learning Engineer, AI Infrastructure Engineer, Cloud Solutions Developer, and AI Automation Specialist.
Yes. Students receive an industry-recognized certification after successfully completing the Cloud AI & MLOps course, practical assignments, and project-based learning activities.
Cloud AI and MLOps are becoming essential for modern Artificial Intelligence development and deployment. Organizations worldwide are adopting scalable AI systems, cloud automation, and production-ready Machine Learning workflows, creating high-demand career opportunities for professionals with Cloud AI and MLOps expertise.
Ready to Scale AI in the Cloud?
Become an MLOps expert and build scalable infrastructure for the next generation of AI applications using cloud, automation, deployment, and DevOps technologies.