Practical Data Science with Amazon SageMaker

Artificial Intelligence and Machine learning are increasingly dominating the business landscape. This makes it important to learn how to collaborate with data scientists to develop ML-integrated applications. As part of this course, you will learn how data scientists develop solutions on the AWS Cloud with Amazon SageMaker. You will also understand how to develop, train and deploy ML models. This course is intended for DevOps Engineers and Application developers eager to develop applications that work well with Machine Learning. Entry-level knowledge of Python programming and basic knowledge of statistics will help. The class is delivered with presentations, hands-on labs and demonstrations by an Amazon Authorized Instructor.


In this course, you will learn to:

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data
  • Summarize the steps a data scientist takes to train ML models
  • Summarize the steps a data scientist takes to evaluate and tune ML models
  • Summarize the steps to deploy a model to an endpoint and generate predictions
  • Describe the challenges for operationalizing ML models
  • Match AWS tools with their ML function

This course is intended for:

  • Development Operations (DevOps) engineers
  • Application developers

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Entry-level knowledge of Python programming
  • Entry-level knowledge of statistics

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo
Why choose Cloud Wizard
  • Advanced Tier Training Partner
  • Amazon Authorised Instructors
  • Official AWS Content
  • Hands-on Labs
Class Deliverables
  • E-Content kit by AWS
  • Hands-on labs
  • Class completion certificates
  • Exam Prep sessions

Dates Available - Click on Book Now to proceed

Classroom1 daysAll DayMay 14, 202415,000
Classroom1 daysAll DayMay 28, 202415,000
Classroom1 daysAll DayJune 11, 202415,000
Classroom1 daysAll DayJune 25, 202415,000
Classroom1 daysAll DayJuly 1, 202415,000
Classroom1 daysAll DayJuly 15, 202415,000

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To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order.

There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.

  1. Training Delivered by an Amazon Authorized Instructor.
  2. AWS Content E-Kit
  3. Hands-on-labs for 30 days
  4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)




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