Machine Learning with Amazon SageMaker

Enable data scientists and engineers to build, train, and deploy scalable machine-learning models on AWS using SageMaker's managed infrastructure and tools.

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Audience: Data scientists and software engineers

Duration: 3 days

Format: Lectures and hands-on labs (≈ 50 % / 50 %)

Overview

Machine Learning is the killer app for Big Data. Amazon SageMaker makes ML a fully managed service that any developer can use. This course balances theory and practice: each ML concept is explained, then implemented in SageMaker with hands-on labs.

Objective

Enable data scientists and engineers to build, train, and deploy scalable machine-learning models on AWS using SageMaker's managed infrastructure and tools.

What You Will Learn

Understand popular ML algorithms, their applicability and limitations
Apply these methods in the Amazon ML environment
Illustrate each algorithm with real-world use-cases implemented in SageMaker

Course Details

Audience: Data scientists and software engineers

Duration: 3 days

Format: Lectures and hands-on labs (≈ 50 % / 50 %)

Prerequisites:

Familiarity with at least one programming language • Ability to navigate Linux command line • Basic AWS familiarity (can be provided on day 1)

Setup: Training AWS account provided • SSH client • Browser • Zero-Install (no software needed on laptops)

Detailed Outline

Data ETL on AWS
Detailed Redshift example
Migration pointers

Goals & results
Supervised vs unsupervised
Which parts AWS implements

Linear regression
Logistic & multinomial regression
SVM
Decision trees & random forests
Neural networks
Labs for each model

K-Means
Hierarchical clustering
Mixture models
DBSCAN

Model visualisation examples
Self-study links

Using built-in algorithms
Bringing your own algorithms
TensorFlow & MXNet
Spark integration
SageMaker libraries

Authentication & access control
Monitoring