9 Hours   7 Sessions   9 Labs   Virtual/Classroom   6-12 Seats  

Content of This Training

An Overview of Amazon SageMaker

  • Content:  We first analyze the pain points of ML practice for a data scientist or a ML engineer, then we introduce SageMaker and the benefit of using SageMaker, followed by a quick overview of AWS AI/ML ecosystem.
  • Duration: 30 mintues
  • Type:  presentation
  • SageMaker Groundtruth (Optional)

  • Content:  Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. In this session, We first introduce the basic components of SageMaker Groundtruth, then we practice a text or image labeling task in the lab.
  • Duration: 45 mintues
  • Type:  presentation and lab
  • SageMaker Notebook and SageMaker Studio

  • Content: An Amazon SageMaker notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources. SageMaker Studio is the first fully integrated development environment (IDE) for machine learning. In this session, We introduce the basic features of SageMaker Notebook and SageMaker Studio, then we have a hands-on lab to practice.
  • Duration: 1 hour
  • Type:  presentation and lab
  • Understanding Built-in Algorithms

  • Content:  Amazon SageMaker comes built-in with a number of high-performance algorithms for different use cases. Learn the fundamentals and then dive deep into how SageMaker implement it. This session covers the topics of model training, tuning and deployment. In the lab sessions, we practice to train, tune and deploy built-in algorithms via both console and programmatical way. We will experiment how you can get the best version of your machine learning model using hyperparameter tuning, how Amazon SageMaker enables you to quickly and easily deploy your ML models to the most scalable infrastructure, the deployment options and autoscaling for your ML models endpoint and how to do the real time and batch inference techniques.
  • Duration: 2 hour
  • Type:  presentation and lab
  • Bring Your Own Custom Models

  • Content:  With Amazon SageMaker, you have the flexibility to bring in your own model and leverage the capabilities of the service. You will dive deep into how you can bring your own model to SageMaker. In the lab, we will practice how to script your model with AWS managed container. We will also warp-up your model in docker container and bring to Sagemaker for training and deployment.
  • Duration: 1.5 hours
  • Type:   presentation and lab
  • SageMaker Model - Debug, Monitor and AutoML

  • Content:  Learn all new feature launched recently which will help you to debug and monitor your ML model. AutoML help to create model for tabular data automatically and SageMaker studio help team to collaborate on model development. In the lab, we will learn to debug your model and perform monitoring in production to prevent inference data drift.
  • Duration: 1 hour
  • Type:   presentation
  • SageMaker Well Architected

  • Content:  Learn how to well architected your ML pipeline with optimization of cost, security, operation, Reliability and performance.
  • Duration: 30 mintues
  • Type:   presentation
  • What is Amazon SageMaker

    The best decription of Amazon SageMaker is from its document:

    Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

    Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.

    Who Needs This Training

    • Someone who wants to use or promote Amazon SageMaker in your organization
    • Participants needs to know some basic AWS knowledge such as S3, EC2, IAM, etc
    • It’s ideal but not required for participants to have a data science or analytics background.

    What This Training Is

    • It analyzes the machine learning pipeline and discusses the benefit of using SageMaker.
    • It provides you with hands-on experience to build, train, and deploy machine learning models quickly
    • It guides you to bring your own model and perform on-premise ML workload Lift-and-Shift to Amazon SageMaker platform.
    • It demonstrates AWS machine learning Well-architect.
    • This training will empower you as an AWS champion, and you can bring the value of AWS back to your organization, even if you are not yet experts in AWS.

    What This Trianing Is Not

    • This training does not bring you to a professional level immediately. The best way to achieve it is learning by doing - applying what you learn from this training on your daily work and keep on practicing.
    • This training is not a substitute for preparing AWS Machine Learning Specialty Certification exam. You can follow the AWS Training and Certification page to prepare the exam.
    • This training is not dedicated to machine learning business use case discovery in your organization. Welcome to talk to me to see how to empower your business by using data science.
    • This training does not cover the topics of machine learning Ops and how to run a ML model in a production environment. If you are interested in these topics, please check our ML Ops on AWS Workshop.


    Step 1: Alignment

    • Align the expectation of this training
    • Confirm the training day, the number of participants, the conference meeting setup, etc

    Step 2: Preparation

    Setup Account

    • You should use a AWS account that is not running production systems.
    • Any accounts should be created a minimum of three days ahead of time. It takes time for new accounts to be completely ready, payment methods to be confirmed, and limits to be set.
    • If participants will share an account between multiple people:
      • Ensure they create IAM accounts for each user that will take labs.
      • Adjust account limits to support the number of students doing labs. Note this process can take up to a week, depending on the specific limits.
      • Check limits for EC2 instances, VPCs (# of VPCs, # of IGWs, # of security groups, EIPs), ELBs, etc. The limits that need to be adjusted depends on the labs that will be delivered.
      • More information about limits can be found on the AWS Service Limits page.


    • Use up to date software: Ensure participants are using a recent version of Edge, Chrome, Firefox, or Safari.
    • Participants using Windows laptops should have a SSH client (like PuTTY) installed.

    Step 3: Training Day

    • Enjoy it :)

    Step 4: Follow-up

    • Post-event clean-up: help participants clean up any resources still running. Ensure that participants not only terminate Sagemaker Notebook instance but also stop any SageMaker deployment endpoint.
    • Let us know your feedback! We are always looking for ways to improve our content for this training.