Description:

This 5-day training provides participants with a deep dive into AWS SageMaker, Amazon’s fully managed machine learning platform. The course starts by introducing AWS machine learning fundamentals, data preparation techniques, and the SageMaker environment. Participants will gain a clear understanding of SageMaker’s built-in algorithms, supported frameworks, and the flexibility to bring their own models.

Through a series of hands-on labs, learners will work with regression, classification, and advanced algorithms such as XGBoost, PCA, Factorization Machines, and DeepAR for time-series forecasting. The training emphasizes practical skills such as preparing datasets, tuning hyperparameters, training and deploying models, and using SageMaker endpoints for real-time inference.

In addition, the course covers integration scenarios with other AWS services, including API Gateway and Lambda, to create production-ready ML pipelines. Learners also explore key AI services such as Comprehend, Translate, Polly, Lex, and Rekognition to broaden their skillset beyond core ML

By the end of the training, participants will be able to design, train, and deploy scalable ML solutions on AWS SageMaker, manage model performance, and apply best practices to build AI-powered applications. The program also provides a strong foundation for professionals preparing for the AWS Machine Learning Specialty certification.

Duration: 5 Days

Course Code: BDT 520

Learning Objectives:

After this training, participants will be able to:

1. Train and deploy ML models using AWS SageMaker
2. Implement advanced ML algorithms (XGBoost, PCA, DeepAR)
3. Integrate SageMaker endpoints with Lambda and API Gateway
4. Leverage AWS AI services for NLP, vision, and speech tasks

1. Data Scientists and ML Engineers
2. Developers exploring AWS machine learning services
3. Cloud Engineers implementing ML pipelines
4. Professionals preparing for AWS ML Specialty certification

1. Basic knowledge of Python
2. Familiarity with AWS fundamentals
3. Understanding of ML concepts (regression, classification)
4. AWS Free Tier account for labs

Course Outline:

Module 1: AWS and ML Foundations

1. AWS ML Specialty Exam Preparation
2. AWS Account Setup and IAM
3. Billing and Monitoring
4. Lab: Setup S3 and SageMaker Notebook

Module 2: ML Concepts and Data Preparation

1. Data Types, Missing Data, Visualization
2. Introduction to Python Notebook
3. Handling mixed data types

Module 3: Introduction to SageMaker

1. Instance Types and Pricing
2. Built-in Algorithms and Frameworks
3. Bring Your Own Algorithm

Module 4: XGBoost with SageMaker

1. Regression and Classification Labs
2. Hyperparameter Tuning
3. Model Deployment with Endpoints

Module 5: PCA and Factorization Machines

1. Dimensionality Reduction
2. MovieLens Recommender
3. Hands-on Demos

Module 6: Time Series with DeepAR

1. Training and Inference Formats
2. Forecasting with Bike Rental Dataset
3. Handling Dynamic Features

Module 7: SageMaker Integration

1. Install SDK and Boto3
2. Lambda and API Gateway Integration
3. Endpoint Deployment

Module 8: Hyperparameter Tuning

1. Tuning Factorization Machines
2. Movie Rating Recommender Lab

Module 9: AWS AI Services

1. Transcribe
2. Translate
3. Comprehend
4. Polly
5. Lex
6. Rekognition
7. Extract

Training material provided: Yes (Digital format)