Deploy and manage AI systems on AWS, Azure, and Google Cloud using modern MLOps practices.

Level: Advanced   Duration: 6 Months

Curriculum

Module 1: Cloud Computing Fundamentals

Tools: AWS, Azure

• Cloud services overview
• Compute resources and virtual machines
• Cloud storage systems
• Networking and VPC concepts
• IAM and security management
• Cloud cost optimization

Module 2: ML on AWS

Tools: SageMaker, Lambda

• SageMaker training workflows
• Model deployment systems
• Batch transform pipelines
• MLOps with SageMaker
• AutoML using Autopilot
• Serverless inference deployment

Module 3: ML on Azure

Tools: Azure ML, Databricks

• Azure ML workspace management
• Compute cluster setup
• Pipeline orchestration
• Model deployment with AKS
• Automated machine learning
• MLflow integration systems

Module 4: ML on Google Cloud

Tools: Vertex AI, BigQuery ML

• Vertex AI training workflows
• Vertex AI pipelines
• Google AutoML systems
• BigQuery ML modeling
• TensorFlow Extended integration
• Cloud Functions for ML

Module 5: MLOps Fundamentals

Tools: MLflow, DVC

• Experiment tracking systems
• Model version management
• Data version control workflows
• Model registry operations
• Reproducible ML pipelines
• Collaborative ML development

Module 6: Containerization & Orchestration

Tools: Docker, Kubernetes

• Docker fundamentals
• Docker Compose workflows
• Kubernetes architecture
• Pods and services management
• Kubeflow pipelines
• Scalable serving architectures

Module 7: Model Serving & Monitoring

Tools: TensorFlow Serving, Seldon

• Model serving patterns
• REST and gRPC APIs
• Real-time inference systems
• A/B testing workflows
• Monitoring ML performance
• Model drift detection

Module 8: CI/CD for ML

Tools: GitHub Actions, Jenkins

• Automated ML testing
• Continuous model training
• Continuous deployment pipelines
• Infrastructure as code
• GitOps for machine learning
• Security and compliance scanning

Career Outcomes

• MLOps Engineer — ₹11,00,000: Build and maintain ML pipelines and infrastructure.
• Cloud ML Engineer — ₹10,50,000: Deploy scalable models on AWS/Azure/GCP.
• ML Platform Engineer — ₹12,50,000: Develop internal tools for data science teams.
• DevOps Engineer (ML) — ₹9,80,000: Automate software and ML delivery workflows.
• Data Engineer (ML) — ₹9,20,000: Build data pipelines for model training.
• ML Infrastructure Architect — ₹15,00,000: Design cloud architecture for AI workloads.
• Production ML Engineer — ₹11,80,000: Focus on model serving and optimization.
• Site Reliability Engineer (ML) — ₹13,00,000: Ensure uptime and reliability of AI systems.
• Cloud Solutions Architect — ₹16,00,000: Design end-to-end cloud AI solutions.
• Principal MLOps Engineer — ₹20,00,000+: Lead MLOps strategy and implementation.

Frequently Asked Questions

What is the Cloud AI & MLOps course?

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.

Who should join the Cloud AI & MLOps course?

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.

Do I need coding or technical knowledge for this course?

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.

What skills will I learn in the Cloud AI & MLOps course?

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.

What is MLOps and why is it important?

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.

Which cloud and AI technologies are covered in this course?

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.

Will I work on real-world Cloud AI and MLOps projects?

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.

What career opportunities are available after completing this course?

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.

Do you provide certification after course completion?

Yes. Students receive an industry-recognized certification after successfully completing the Cloud AI & MLOps course, practical assignments, and project-based learning activities.

Why should I learn Cloud AI & MLOps today?

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.

About IAIACENTER.IN

The Institute of Artificial Intelligence Applications Center (IAIACENTER.IN) is a leading AI education and training institution based in Bhubaneswar, Odisha, India. Specializing in industry-oriented Certificate, Diploma, and Professional courses, IAIACENTER.IN is dedicated to building practical capabilities in Artificial Intelligence and emerging technologies. IAIACENTER.IN’s programs cover a wide spectrum of AI domains, including Artificial Intelligence, Machine Learning, Natural Language Processing (NLP), Deep Learning, Computer Vision, Cloud AI and MLOps, AI in Cybersecurity, Software Development, Data Science and Analytics, and Robotics and Automation. The institution also offers sector-specific AI training to empower professionals and organisations to apply AI in their respective fields. These tailored programs serve entrepreneurs, healthcare professionals, accountants and finance professionals, teachers and educators, content creators and media professionals, office and management professionals, students, and emerging professionals. Each program focuses on practical AI applications that enhance productivity and innovation, and promotes responsible adoption and effective integration of AI into professional workflows. Positioned as India’s first Safe, Trusted, and Reliable AI Applications Centre, IAIACENTER.IN is deeply committed to promoting Responsible AI across society. The institution emphasises the safe, ethical, transparent, reliable, and human-centred use of artificial intelligence, aiming to bridge the gap between rapidly evolving AI technologies and their responsible application in education, business, government, media, healthcare, and daily life.