AWS SageMaker Machine Learning in 4 Hours
Published by Pearson
From your first steps to model training and deployment
Machine learning is becoming more complex every day. At the same time, core tools have appeared to make the life of data scientists easier. Amazon SageMaker is one of these tools. This online service allows you to build Machine Learning models using AWS SageMaker. This 4-hour live training is full of hands-on live demos that will let you see in real-time how to train and deploy machine learning models for a large variety of use cases.
Whether you want to learn machine learning (using a large set of Amazon pre-trained data, models and algorithms) or to accelerate your model development, SageMaker offers an environment that will make your ML projects easier to run and develop. In this class, you will get familiar with SageMaker, discover its suite of tools, and engage in practical examples on how you can use the suite to become more ML-efficient.
What you’ll learn and how you can apply it
By the end of the live online course, you’ll understand:
- The benefits of Amazon SageMaker and how it works
- The SageMaker suite of tools and how to navigate them
- SageMaker models and pre-trained algorithms and how they can accelerate your ML project development
And you’ll be able to:
- Prepare and clean data with SageMaker
- Train a ML model with SageMaker
- Deploy your trained model
This live event is for you because...
- You run machine learning projects on your laptop, but want to discover ML professional solutions
- You are a data scientist in search of a platform that can handle large projects
- You are curious about machine learning in general, and are looking for a platform to experiment
Prerequisites
- Basic knowledge about machine learning is useful but not required.
Course Set-up
- If you would like to follow along with the examples in the course, please sign up for an AWS account (https://portal.aws.amazon.com/billing/signup#/start/email)
Recommended Preparation
- Watch: Data Analytics and Machine Learning Fundamentals LiveLessons (Video Training) by Robert Barton and Jerome Henry
- Attend: Fundamentals of Machine Learning and Data Analytics by Robert Barton and Jerome Henry
Recommended Follow-up
- Read: Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists, 2nd Edition by Julien Simon
- Read: Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments by Joshua Arvin Lat
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Getting to know SageMaker (25 minutes)
- Types of Machine Learning
- The Machine Learning Workflow
- AWS Machine Learning Solutions
- Where SM fits in Machine Learning, and in AWS
Segment 2: SageMaker walkthrough (30 min)
- A vertical view of SageMaker and its components
- A horizontal view of SageMaker and its components
- Demo: Getting started (account creation and environment setup)
Break (10 min)
Segment 3: Data preparation with SageMaker (25 min)
- Defining training and test sets
- Creating a notebook (Jupyter)
- Loading a dataset
- Exploring data properties
- Demo: working on pre-set datasets
Segment 4: data analysis in SageMaker (25 min)
- Visualizing the dataset
- Exploring relationship between features and variables
- Good vs. Bad data
- Demo: Cleaning the data
Break (10 min)
Segment 5: Training a Machine Learning model (20 min)
- Types of algorithms in SageMaker (+ TensorFlow, Pytorch
- Preparing the model training set
- Training the model
- Checking the training result
Segment 6: training use cases (40 min)
- Linear regression example (demo)
- Unsupervised learning example (demo)
- Image classification example with low level SDK (demo)
- Image classification example with high level built-in library (demo)
Segment 7: Tuning a Machine Learning model (15 min)
- Automatic tuning in SageMaker
- Demo: model tuning
Segment 8: Deploying models and production readiness (20 min)
- Testing the deployment in AWS hosting service
- Deploying your trained model to an endpoint
- Scaling the deployed model
Q&A: 20 mins
Your Instructors
Jerome Henry
Jerome Henry is a Distinguished Engineer in the Office of the Wireless CTO at Cisco Systems. His main field of research is around optimization of performances in unlicensed wireless networks, which includes aspects of QoS, IoT, privacy, indoor location, but also AI/Machine Learning and LLMs centered on network languages. Jerome has more than 25 years of experience teaching technical courses in more than 15 different countries and 4 different languages, to audiences ranging from graduate degree students to networking professionals and technical support engineers. Jerome joined Cisco in 2012. Before that time, he was consulting and teaching heterogeneous networks and wireless integration with the European Airespace team, which was later acquired by Cisco to become their main wireless solution.
Rob Barton
Rob Barton is a Distinguished Engineer with Cisco. Rob has worked in the IT industry for over 27 years, the last 25 of which have been with Cisco. Rob Graduated from the University of British Columbia with a degree in Engineering Physics. Rob is a published author, with titles on subjects of Generative AI, Quality of Service (QoS), Wireless Communications, and IoT. Additionally, he has co-authored many peer-reviewed research papers and leads Cisco’s academic research partnership program. Rob holds numerous patents in the areas of AI, wireless communications, network security, cloud networking, and IoT. His current areas of work include network automation and Agentic models for IT management.