Introduction

As organizations increasingly adopt cloud technologies, Amazon Web Services (AWS) has emerged as a leading platform for scalable, secure, and cost-effective data science solutions. This course provides a comprehensive, hands-on approach to building, deploying, and managing data science workflows using AWS services like Amazon S3, AWS Glue, Amazon SageMaker, AWS Lambda, and AWS Step Functions.

Participants will learn how to leverage AWS for big data processing, model training, and real-time analytics, gaining practical experience in cloud-based machine learning, data engineering, and AI-driven solutions.

Course Objectives

By the end of this course, participants will be able to:

• Understand AWS cloud architecture for data science workloads.
• Work with Amazon S3, AWS Glue, and AWS Lambda for data ingestion and transformation.
• Build scalable machine learning models using Amazon SageMaker.
• Use AWS Athena and Redshift for big data analytics.
• Deploy serverless and containerized machine learning workflows with AWS Lambda and SageMaker.
• Optimize data pipelines using AWS Step Functions and EventBridge.
• Implement MLOps practices for production-ready AI solutions on AWS.

Who Should Attend?

This course is ideal for:

• Data scientists looking to scale ML models on the cloud.
• Data engineers managing cloud-based data pipelines.
• AI/ML engineers deploying models in production.
• Software developers integrating ML workflows with AWS.
• Cloud architects designing end-to-end AI-driven solutions.

Day-by-Day Course Breakdown

Day 1: AWS Fundamentals & Data Science Workflow

Introduction to AWS for Data Science

• Overview of AWS services for data science & AI
• Understanding cloud storage, compute, and networking in AWS
• Setting up an AWS environment: IAM roles, security best practices

Data Storage & Management on AWS

• Amazon S3: Data storage, versioning, and access control
• AWS Glue: ETL processing and data cataloging
• Hands-on lab: Storing and retrieving datasets in Amazon S3

Day 2: Data Processing & Feature Engineering

Big Data Processing with AWS

• Introduction to AWS Glue for large-scale data transformation
• Using AWS Lambda for serverless data processing
• Hands-on lab: ETL pipeline using Glue, Lambda, and S3

SQL & Analytics on AWS

• Amazon Athena: Running SQL queries on S3 data
• AWS Redshift: Data warehousing for analytics
• Hands-on lab: Running big data queries with Athena & Redshift

Day 3: Machine Learning with Amazon SageMaker

Building ML Models in SageMaker

• Overview of SageMaker architecture and workflow
• Data preprocessing, training, and hyperparameter tuning
• Hands-on lab: Training a machine learning model in SageMaker

Deploying & Scaling ML Models

• SageMaker endpoints: Real-time & batch inference
• Model monitoring & A/B testing with SageMaker Model Monitor
• Hands-on lab: Deploying an ML model as an API endpoint

Day 4: Serverless & MLOps on AWS

Building Serverless ML Pipelines

• AWS Lambda, API Gateway, and Step Functions for automation
• Event-driven data pipelines with AWS EventBridge
• Hands-on lab: Automating ML model retraining with Lambda & Step Functions

MLOps & CI/CD for Machine Learning

• Implementing MLOps with AWS CodePipeline and SageMaker Pipelines
• Model versioning, monitoring, and retraining strategies
• Hands-on lab: Deploying an automated ML workflow using SageMaker Pipelines

Day 5: Advanced Topics & Capstone Project

Deep Learning & AI on AWS

• GPU-based training with AWS EC2 & SageMaker
• Running deep learning models on AWS Inferentia
• Hands-on lab: Training a deep learning model on AWS

Capstone Project: End-to-End AI Pipeline on AWS

• Participants will design and deploy a complete AI/ML pipeline
• Data ingestion, processing, model training, deployment, and monitoring
• Final presentations and peer reviews

Conclusion & Certification

At the end of the training, participants will receive a Certificate of Completion, validating their expertise in Data Science on AWS.

This course blends hands-on labs, real-world case studies, and industry best practices to prepare participants for cloud-based AI and data science challenges.