Login
Introduction to AWS Cloud-Based Machine Learning & Intelligent System
Artificial Intelligence & Data Analytics

Introduction to AWS Cloud-Based Machine Learning & Intelligent System

This course introduces students to the fundamentals of AWS cloud computing, machine learning, and intelligent systems. It explores AWS services and tools for data storage, processing, model development, training, and deployment. Students gain practical insights into building and implementing scalable, cloud-based machine learning solutions for real-world applications.
Course ModeOnline
Duration2 Weeks
Starts14 Dec 2026
Ends26 Dec 2026
Course Fee
₹1,000 /-
Enroll Now
About The Course

Overview

The course “Introduction to AWS Cloud-Based Machine Learning and Intelligent Systems” provides students with a comprehensive understanding of cloud computing concepts, machine learning technologies, and the development of intelligent applications using Amazon Web Services (AWS). It introduces learners to the role of cloud platforms in enabling scalable, flexible, and cost-effective computing solutions for modern technological challenges.

The course explores essential AWS services for data storage, processing, streaming, and deployment, along with an introduction to machine learning workflows, including data preparation, model training, evaluation, and deployment. Students become familiar with relevant AWS tools and services, such as Amazon S3, AWS Lambda, Amazon SageMaker, and container-based deployment technologies, to understand how cloud infrastructure supports intelligent systems.

Furthermore, the course emphasizes the practical integration of cloud computing and machine learning to develop solutions for real-world applications. Learners gain insights into intelligent automation, data-driven decision-making, scalable AI solutions, and the deployment of machine learning models in cloud environments. Through conceptual learning and practical exposure, students develop foundational skills to understand, design, and implement cloud-based intelligent applications.

By the end of the course, students are expected to understand the fundamental principles of AWS cloud computing, identify suitable cloud services for machine learning tasks, explain the machine learning development lifecycle, and recognize the importance of scalability, reliability, security, and efficient resource utilization in intelligent systems. The course establishes a strong foundation for further learning in cloud architecture, artificial intelligence, machine learning, and cloud-native application development.

Learning Goals

Course Outcomes

After completing the course, students will be able to:

  1. CO1: Explain cloud computing fundamentals, AWS architecture, and essential cloud security principles.

  2. CO2: Identify and describe AWS services for storage, computing, data processing, and deployment.

  3. CO3: Apply fundamental machine learning concepts to understand data preparation, model training, and evaluation.

  4. CO4: Explain the use of AWS machine learning services for developing and deploying intelligent applications.

  5. CO5: Design a basic cloud-based machine learning workflow while considering scalability, security, cost, and responsible AI practices.

Curriculum

Course / Modules

The course is structured into six modules, progressing from cloud computing fundamentals to the development and deployment of cloud-based machine learning applications.

Module 1: Fundamentals of Cloud Computing and AWS

  • Introduction to cloud computing, its characteristics, benefits, and service models: IaaS, PaaS, and SaaS.

  • Cloud deployment models: public, private, hybrid, and community cloud.

  • Overview of Amazon Web Services (AWS), its global infrastructure, Regions, Availability Zones, and Edge Locations.

  • Introduction to the AWS Management Console and AWS account navigation.

  • Fundamentals of cloud security, the Shared Responsibility Model, and AWS Identity and Access Management (IAM).

  • Overview of AWS pricing, billing, and cost optimization.

Module 2: AWS Storage, Computing, and Data Processing Services

  • Introduction to Amazon Simple Storage Service (Amazon S3) for cloud-based object storage.

  • Fundamentals of Amazon Elastic Compute Cloud (Amazon EC2) for virtual computing resources.

  • Introduction to AWS Lambda and serverless computing.

  • Overview of Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS).

  • Introduction to data ingestion, processing, and streaming using AWS services such as Amazon Kinesis.

  • Understanding the role of cloud storage and computing resources in machine learning workflows.

Module 3: Fundamentals of Machine Learning and Intelligent Systems

  • Introduction to artificial intelligence, machine learning, and intelligent systems.

  • Types of machine learning: supervised, unsupervised, and reinforcement learning.

  • Understanding datasets, features, labels, training data, and testing data.

  • Data preprocessing, cleaning, transformation, and feature engineering.

  • Introduction to machine learning algorithms, including regression, classification, and clustering.

  • Model training, validation, evaluation, and performance metrics.

  • Applications of intelligent systems in healthcare, education, finance, manufacturing, and automation.

 

Module 4: AWS-Based Machine Learning Tools and Services

  • Introduction to Amazon SageMaker and its role in the machine learning lifecycle.

  • Understanding cloud-based machine learning environments and development tools.

  • Preparing and uploading datasets to Amazon S3 for machine learning tasks.

  • Introduction to model development, training, testing, and evaluation using AWS tools.

  • Overview of built-in algorithms and supported machine learning frameworks.

  • Introduction to managed AI services offered by AWS.

  • Understanding the benefits of cloud-based machine learning, including scalability, accessibility, and resource efficiency.

 

Module 5: Model Deployment, Scalability, and Intelligent Applications

  • Introduction to deploying trained machine learning models in cloud environments.

  • Understanding inference, prediction, and model endpoints.

  • Introduction to real-time and batch inference.

  • Integrating machine learning models with applications using APIs and AWS Lambda.

  • Fundamentals of container-based deployment using Amazon ECS and Amazon EKS.

  • Monitoring model performance, resource utilization, and application reliability.

  • Introduction to model versioning, updates, and basic MLOps concepts.

  • Understanding scalability, availability, and performance optimization for intelligent applications.

 

Module 6: Practical Applications, Security, and Responsible AI

  • Designing an end-to-end cloud-based machine learning workflow.

  • Developing introductory applications such as student performance prediction, sentiment classification, or basic image classification.

  • Understanding cloud security, access control, data privacy, and secure data handling.

  • Introduction to ethical AI, algorithmic bias, transparency, and responsible use of intelligent systems.

  • Understanding AWS cost management and responsible resource utilization.

  • Practical case studies involving cloud-based intelligent applications.

  • Mini-project: Design and demonstrate a basic machine learning application using appropriate AWS services.

Need Help?

Frequently Asked Questions

The course aims to introduce students to AWS cloud computing, machine learning fundamentals, and intelligent systems. It helps learners understand how cloud services can be used to develop, train, and deploy scalable machine learning applications.
No. The course is designed for beginners. Basic computer knowledge and an introductory understanding of programming are helpful, but prior experience with AWS or machine learning is not mandatory.
Students will explore key AWS services, including Amazon S3 for storage, Amazon EC2 for computing, AWS Lambda for serverless execution, Amazon SageMaker for machine learning, and Amazon ECS/EKS for container-based deployment.
Students will gain foundational skills in cloud resource management, data preparation, machine learning workflows, model training and evaluation, and cloud-based deployment. They will also learn to design basic intelligent applications while considering scalability, security, and cost efficiency.