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Cloud Computing with Applied AI

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Cloud Computing With Applied AI

Unlock the power of Cloud Computing and Applied AI with our comprehensive online program. Learn how to design scalable cloud systems, integrate AI models, and deploy intelligent solutions across industries like healthcare, finance, and e-commerce. Through hands-on projects, live case studies, and practical tool training, you’ll master both cloud platforms and AI applications. This course takes you from the fundamentals of cloud to advanced AI-driven architectures, preparing you to stand out in the fastest-growing tech field.

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Download CurriCulum

6 Months

.Duration

Virtual Live

Classes

100% Job

Assurance

Languages

English | Hindi

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Course Highlights

Live Expert-Led Training

Learn directly from cloud & AI professionals

Hands-On Projects

Work on AWS, Azure & Google Cloud with real datasets

Cloud + AI Fusion

Build & deploy intelligent applications on the cloud

Career Support

Resume building, mock interviews & placement guidance

Master these Tools

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Skills Covered

Cloud Security & Compliance

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Serverless Computing & Microservices

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API Integration & Cloud Automation

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Containerization & Orchestration

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AI Model Deployment on Cloud

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Cloud Infrastructure Management

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Course Curriculum

The Cloud Computing with Applied AI Mastery Course covers everything from cloud platforms and virtualization to AI-powered services, big data, and serverless computing. You’ll master Docker, Kubernetes, and MLOps for deploying AI at scale. With hands-on projects and real-world case studies, you’ll gain the skills to design and manage intelligent cloud solutions for a successful career in Cloud & AI.

  • • Overview of Cloud Computing
    • History and evolution of cloud technology
    • Cloud service models: IaaS, PaaS, SaaS
    • Cloud deployment models: Public, Private, Hybrid, Multi-cloud
    • Overview of Artificial Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Use cases of AI in cloud computing
    • Benefits and challenges of cloud AI integration
  • • Cloud architecture and components
    • Virtualization basics: Hypervisors, VMs
    • Containers vs Virtual Machines
    • Storage fundamentals: Block, File, Object storage
    • Networking in the cloud: VPC, Subnets, Security Groups
    • Monitoring and logging
    • Cost management and optimization
  • • Amazon Web Services (AWS) fundamentals
    • Microsoft Azure fundamentals
    • Google Cloud Platform (GCP) fundamentals
    • Comparison of cloud providers and services
    • Hands-on labs: Creating accounts, setting up free-tier resources
  • • Virtual Machines in cloud
    • Auto-scaling and load balancing
    • Serverless computing: AWS Lambda, Azure Functions, Google Cloud Functions
    • Kubernetes basics and orchestration
    • Docker containers for AI workloads
    • Hands-on labs: Deploying containerized applications
  • • Object storage: S3, Azure Blob, GCP Storage
    • Relational databases: RDS, Cloud SQL
    • NoSQL databases: DynamoDB, Cosmos DB, Firestore
    • Data lakes and data warehouses: Redshift, BigQuery, Synapse
    • Security, backup, and disaster recovery
    • Hands-on labs: Creating storage buckets and databases
  • • Virtual networks and subnets
    • Firewalls and security groups
    • VPNs and Direct Connect
    • Identity and Access Management (IAM)
    • Encryption in transit and at rest
    • Best practices for cloud security
  • • AI/ML lifecycle: Data collection → Training → Deployment
    • Supervised vs Unsupervised learning
    • Deep Learning fundamentals: Neural Networks, CNNs, RNNs
    • NLP basics: Tokenization, Embeddings, Transformers
    • AI frameworks: TensorFlow, PyTorch, Scikit-learn
    • Hands-on labs: Building simple AI models
  • • AWS AI services: SageMaker, Rekognition, Comprehend, Lex
    • Azure AI services: Azure ML, Cognitive Services, Bot Services
    • GCP AI services: Vertex AI, Vision AI, NLP API
    • AutoML and pre-trained models
    • Hands-on labs: Deploying AI models on cloud
  • • Data ingestion, ETL, and preprocessing
    • Streaming data processing: Kinesis, Pub/Sub
    • Feature engineering and selection
    • Model training and versioning
    • Model deployment: Batch vs Real-time inference
    • Hands-on labs: Building AI pipelines in cloud
  • • MLOps concepts and lifecycle
    • Continuous integration & continuous deployment (CI/CD) for AI
    • Monitoring AI model performance in production
    • Model retraining and updates
    • Tools: MLflow, Kubeflow, Seldon
    • Hands-on labs: Deploying ML models with CI/CD pipelines
  • • Serverless architecture principles
    • Event-driven AI workflows
    • Integrating AI APIs with serverless functions
    • Scalable AI inference pipelines
    • Hands-on labs: Building a serverless AI chatbot
  • • Edge computing basics
    • AI at the edge vs cloud AI
    • IoT devices and data streams
    • Integrating IoT with cloud AI
    • Hands-on labs: Deploying edge AI models with cloud support
  • • Infrastructure as Code (IaC): Terraform, CloudFormation
    • CI/CD pipelines for cloud deployments
    • Monitoring and logging with CloudWatch, Azure Monitor, Stackdriver
    • Automated scaling and orchestration for AI workloads
  • • AI-powered cloud security
    • Generative AI on cloud platforms
    • Multi-cloud AI strategies
    • Quantum computing and AI in cloud
    • Real-world case studies and industry applications
  • • Capstone Project:
     – Build and deploy an AI-powered web application on cloud
     – Integrate data pipelines, model training, and serverless deployment
    • Documentation & Presentation of project
    • Career Opportunities:
     – Cloud Engineer, AI Engineer, ML Engineer, DevOps Engineer, Data Engineer
    • Certification Guidance:
     – AWS Certified Machine Learning – Specialty
     – Microsoft Azure AI Engineer Associate
     – Google Professional Machine Learning Engineer
    • Resume & Interview Preparation
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About Cloud Computing With Applied AI Course

Our Cloud Computing with AI Integration Program is designed to equip you with the skills needed to design, deploy, and scale intelligent cloud solutions. You’ll master cloud platforms (AWS, Azure, GCP), virtualization, containerization, big data processing, and AI-powered cloud services—empowering you to build real-world applications, accelerate your career, or launch your freelancing journey.

✅ Practical, Project-Based Learning
Through hands-on labs, live projects, and real-world case studies, you’ll gain practical experience in cloud deployment, serverless computing, and MLOps. You’ll graduate with a strong portfolio demonstrating your ability to build and manage AI-driven cloud systems, making you job-ready for high-demand roles.

✅ Expert Mentorship & Career Support
Learn directly from industry experts, get personalized career guidance, prepare for certifications (AWS, Azure, GCP), and ace technical interviews. Plus, earn a recognized certification to stand out to employers and clients in the competitive Cloud & AI space.

✅ For Everyone Ready to Master Cloud + AI
Whether you’re a student, IT professional, or entrepreneur, this program will help you master cloud infrastructure, AI services, automation, and scalable deployments—preparing you for the future of intelligent cloud solutions.

👥 Who Should Enroll?
✔ Students & graduates aiming to start a career in Cloud, DevOps, or AI-driven roles
✔ Working professionals looking to upskill or switch to Cloud + AI careers
✔ Entrepreneurs & tech founders wanting to integrate AI into cloud-based products & servicesv

Earn Industry Recognised Certificates from:

Career Opportunities After the Course

List potential roles students can pursue after completing the course, such as
  • Cloud Engineer
  • MLOps Engineer
  • Cloud Solutions Architect
  • DevOps Engineer (Cloud & AI
  • Cloud Security Specialist
  • Freelance Cloud & AI Consultant
  • AI/ML Cloud Engineer

With 100% job assurance, our course prepares you for a lucrative career in Cloud Computing and AI Integration.

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Live Virtual Classes

Batch Timings

📅 Weekday Batches
🕖 Monday to Friday | 7:00PM – 9:00PM

📅 Weekend Special Batches
🕖 Saturday | 4:00 PM – 9:00 PM
🕖 Sunday | 10:00 AM – 3:00 PM

Total Course Duration : 6 Months

Call to Action:

Live Virtual Classes

Course Starts At:

₹17,999

EMI Options Available

Starts At ₹3500 / month

Our Alumni Works At

Alumni's Testimonies

The integration of AI with cloud platforms gave me a unique edge. The hands-on projects on AWS and Azure not only built my confidence but also impressed recruiters. Today, I’m working on real AI-powered cloud applications.
Singh Durgesh Anil
MLOps Engineer
What I loved most was the focus on practical learning. Docker, Kubernetes, and MLOps were taught with real use cases. Within 3 months of completing the course, I started freelancing and now work with international clients on cloud-based AI projects
Sharvi Satish Garg
Cloud Engineer
"This course gave me end-to-end clarity on data engineering, cloud deployment, and AI services. The career support team helped me polish my resume and prepare for interviews, which led me to my current role at Capgemini.
Ravindra Raju Deshmukh
Software Engineer

Frequently Asked Questions

About Us

At HITMI, we’re committed to delivering cutting-edge education across today’s most in-demand tech domains. With hands-on training, real-world tools, and 100% job assistance, our programs empower individuals to thrive in the fast-paced digital economy.Whether you’re starting fresh or upskilling, our expert-led courses help you stay ahead in the world of tech and innovation.

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  • +91 9987210168
  • hitmiindia@gmail.com

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