Machine Learning with Amazon SageMaker Training Course

Who should attend

This course will be useful for anyone who needs to create machine learning solutions to analyse data and is looking to benefit from Amazon's cloud-based data science platform.

Prerequisites

Delegates should have some familiarity with working with data, for instance MS Excel. Any experience of SQL or NoSQL databases would be useful but not mandatory, as would experience of a programming language such as Python, C#, Java

Live, instructor-led online and on-site training

We appreciate that you need flexibility to fit in with new working situations - whether you're an individual, part of a distributed team, or simply have projects and deadlines to meet. 

Our remote training can take place online in a virtual classroom, with content split into modules to accommodate your scheduling challenges and meet your learning goals. Get in touch today to find out how we can help design a cost-effective, flexible training solution.

As soon as it's safe, we'll return to also offering the on-site custom training courses and programmes upon which we've built our reputation.

Introduction 

• Overview of key Machine Learning and Deep Learning concepts
• Getting about in AWS
• Overview of SageMaker features
• Taking your first look at SageMaker studio

Preparing your dataset

• Identifying your data and articulating your problem
• Format data for consistency
  
• Cleaning and validating your data
• Uploading to SageMaker

Data Analysis

• Clustering
• Trend analysis
• Finding other relationships between different types of data  

Data Visualisation

• Frequency tables
• Cross-tabulation tables
• Bar charts
• Line graphs
• Pie charts
• Heat Maps
• Scatter graphs.

Training your Model

• Creating a Training job
• Assigning Compute resources
• Selecting the right algorithm
• Overview of using custom code (Python, TensorFlow) 

Deploying your Model

• SageMaker Hosting Services
• Configuring and creating an HTTPS endpoint

SageMaker Batch Transforms

• Making inferences from your dataset
• Indexing and real-time indices 
• Using Batch Transform to preprocess data to train a new model

Validating your Model

• SageMaker Debugger
  
• Offline testing
• Online testing
• Validating using a holdout set
  

Model Tuning

• Defining metrics
  
• Hyperparameter tuning
  
• Automatic model tuning 

Deploying and sharing using SageMaker Feature Store

• Creating, Storing and Sharing Features
• Online / Offline
• Feature Groups
• Discovery
• Batch Inference
• Feature Data Ingestion