Practical Data Science with Amazon SageMaker

  • Tuition USD $200 Save $475
  • Reviews star_rate star_rate star_rate star_rate star_half 839 Ratings
  • Course Code AWS-PDSASM
  • Duration 1 day
  • Available Formats Classroom, Virtual

Prerequisite Courses

AWS Technical Essentials

In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.

Skills Gained

  • Prepare a dataset for training
  • Train and evaluate a Machine Learning model
  • Automatically tune a Machine Learning model
  • Prepare a Machine Learning model for production
  • Think critically about Machine Learning model results

Who Can Benefit

  • Developers
  • Data Scientists

Prerequisites

Course Details

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
  • Demo: SageMaker console
  • Demo: 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

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

Module 5: Training and Evaluating a Model

  • Types of Algorithms
  • XGBoost and SageMaker
  • Demo 5: Training the data
  • Exercise 3: Finishing the Estimator definition
  • Exercise 4: Setting hyperparameters
  • Exercise 5: Deploying the model
  • Demo: Hyperparameter tuning with SageMaker
  • Demo: Evaluating Model Performance

Module 6: Automatically Tune a Model

  • Automatic hyperparameter 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 Scaling
  • Demo: Configure and Test Autoscaling
  • Demo: Check Hyperparameter tuning job
  • Demo: AWS Autoscaling
  • Exercise 10-11: Set up AWS Autoscaling

Module 8: Relative Cost of Errors

How do I enroll?

A comprehensive listing of ExitCertified courses can be found here. You can register directly for the required course/location when you select "register". If you have any questions or prefer to speak with an ExitCertified education consultant directly, please submit your query here. A representative will contact you shortly.

How do I pay for a class?

You can pay at the time of registration using credit card (Mastercard/Visa/American Express) cheque or PO.

What if I have training credits?

ExitCertified honors all savings programs from the partners we work with. ExitCertified also offers training credits across multiple partners through our FLEX Account.

When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Fantastic instructor, good overview of content and ability to answer questions

I took the AWS Technical Essentials and it was taught very well. It was very comprehensive and the instructor was fantastic. Already having familiarity with AWS, it was still taught at a level where I felt like it was worth taking before doing taking the AWS Developer course.

The course for Cognos Enterprise Admin was wonderful! Learned a lot and have started applying what was learned. Thanks.

Very good class. Ken (the instructor) is very knowledgeable and has great industry experience as well

Outstanding job on the facilitator of this course (Ruben) and great information taught throughout the duration!

Ruben did a fantastic job facilitating this course! The only downfall is trying to cram all this information into 2 days! Incredibly overwhelming...

Related Certifications

Authorized training from ExitCertified is created and maintained by the vendor who also creates the certification exams. While it may not be a requirement of certification to attend a vendor-authorized training class, doing so will put you in the best position to successfully complete the related exams. Start training and begin working towards one of the following certifications today.

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AWS Certified Machine Learning - Specialty

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Follow-on Courses

Browse the suggested follow-on courses below and continue your training journey. We offer training in various learning formats, from instructor-led to virtual, so you can choose what works best for you. Get started by selecting a course to learn more.

The Machine Learning Pipeline on AWS
Comprehensive Machine Learning

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