CERTIFICATE IN CLOUD COMPUTING PROFESSIONAL WITH ARTIFICIAL INTELLIGENCE

Category: COMPUTER

Course Overview:

This comprehensive 6-month program is designed to equip learners with in-depth knowledge and practical skills in Cloud Computing and Artificial Intelligence (AI). The course focuses on real-world applications of cloud services such as AWS, Microsoft Azure, and Google Cloud Platform, along with the integration of AI tools and machine learning for intelligent automation and business optimization.
Students will gain hands-on experience in cloud deployment, management, data analytics, cybersecurity, and AI model deployment in cloud environments.

Course Objectives:

• Understand core concepts of Cloud Computing and service models (IaaS, PaaS, SaaS).

• Learn cloud deployment and management using AWS, Azure, and Google Cloud.

• Develop, train, and deploy AI and ML models on cloud platforms.

• Explore cloud security, compliance, and monitoring mechanisms.

• Gain hands-on exposure to DevOps tools, Docker, Kubernetes, and APIs.

Course Structure:

Module 1: Introduction to Cloud Computing (2 Weeks)

• Cloud Fundamentals: Architecture, Deployment, and Service Models

• Virtualization Concepts: Hypervisors and Virtual Machines

• Cloud Providers Overview: AWS, Azure, GCP

• Setting up Free Cloud Accounts and Basic Operations

Module 2: Cloud Infrastructure and Services (4 Weeks)

• Compute, Storage, and Networking in the Cloud

• Cloud Management Tools and Dashboards

• Load Balancing and Auto Scaling

• Cloud Databases (RDS, DynamoDB, Firestore, SQL, NoSQL)

• Serverless Computing Concepts

Module 3: Artificial Intelligence and Machine Learning Fundamentals (4 Weeks)

• Introduction to AI, ML, and Deep Learning

• Python for AI – NumPy, Pandas, Matplotlib, TensorFlow

• Data Preprocessing, Training, and Evaluation

• Building Predictive Models

• AI Use Cases in Cloud Environments

Module 4: AI Integration with Cloud Platforms (4 Weeks)

• AWS AI/ML Services (SageMaker, Rekognition, Comprehend)

• Google Cloud AI (Vertex AI, AutoML, Vision, NLP)

• Microsoft Azure Cognitive Services

• Deploying AI Models on Cloud Instances

• Cloud Automation using AI

Module 5: DevOps, Containers, and Cloud Security (4 Weeks)

• Introduction to DevOps and CI/CD Pipelines

• Docker and Kubernetes Essentials

• Cloud Security Principles and Identity Management (IAM)

• Threat Analysis and Data Protection

• Backup, Disaster Recovery, and Compliance

Module 6: Capstone Project & Certification (2 Weeks)

• Real-world project: Deploying an AI-Powered Application on Cloud

• Performance Optimization and Cost Management

• Final Assessment and Industry-Oriented Presentation

Learning Outcomes:

Upon completion, learners will be able to:
 Deploy and manage scalable applications on the cloud.
 Build, train, and integrate AI models using cloud-based services.
 Understand DevOps practices and secure cloud environments.
 Gain job-ready skills for roles like Cloud Engineer, AI Developer, Cloud Administrator, or Data Analyst.