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Machine Learning with Amazon SageMaker

Machine Learning

Duration: 2 Days


Machine learning with Amazon SageMaker is for someone with basic knowledge of Machine Learning concepts. Machine Learning is the brain behind business intelligence. Through Machine Learning applications, business can better understand the consumer’s preferences and take smart decisions thus increase their profits. This course focuses on running Machine Learning experiments on AWS Platform.

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  • To achieve in depth understanding of Machine Learning experimentation on AWS platform
  • To gain knowledge of various Machine Learning algorithms, understanding problems & applying practical solutions

Who Should Attend

  • Professionals working in various industries who want to formally start working in Machine Learning job profiles with AWS platform
  • Machine Learning & AWS enthusiasts


Course Outline

Day 1

  1. Amazon SageMaker: Introduction to Amazon SageMaker
    Hands-on: Getting started with Amazon SageMaker
  2. Using Built-in Algorithms
    • Common Parameters
    • Linear Learner
    • K – means Algorithm
    • Logging with CloudWatch
    • Logging Amazon SageMaker API Calls with AWS CloudTrail
    • Parameters / Data formats
    • Various Algorithms

    Hands-on: Using Amazon SageMaker Built-in Algorithms

  3. Docker with SageMaker: Running Custom Algorithms
    • Using Your Own Training Algorithms
    • Using Your Own Inference Code

    Hands-on: Running Custom Algorithms with Amazon SageMaker

  4. Authentication and Access Control
    • Overview of Managing Access
    • Using Identity-Based Policies (IAM Policies)
    • Amazon SageMaker API Permissions Reference
    • Amazon SageMaker Roles
  5. Monitoring
    • Monitoring with CloudWatch
    • Logging with CloudWatch
    • Logging Amazon SageMaker API Calls with AWS CloudTrail

Day 2

  1. Automatically Scaling Amazon SageMaker Models
    • Configure Automatic Scaling for a Variant
    • Editing a Scaling Policy
    • Deleting a Scaling Policy
    • Load Testing
    • Additional Considerations
    • Amazon SageMaker Libraries

    Hands-on: Scaling Amazon SageMaker Model

  2. Amazon SageMaker with Apache Spark & EMR
    • Introduction to Hadoop Ecosystem and Amazon EMR
    • Getting SageMaker Spark
    • Running SageMaker Spark
    • Amazon Record Format
    • Serializing and Deserializing for Inference
    • SageMaker Spark with SageMaker Algorithm
    • SageMakerEstimator and SageMakerModel in a Spark Pipeline

    Hands-on: Apache Spark with Amazon SageMaker

  3. Apache MXNet with Amazon SageMaker
    • Basics of Apache MXNet
    • Tools with MXNet

    Hands-on: Apache MXNet with Amazon SageMaker

About the Trainer

Shivam Sharma

Shivam Sharma
Subject Matter Expert

Shivam works as Subject Matter Expert with CloudThat Technologies and is passionate about ever evolving technologies he works on like Machine Learning, Azure and Blockchain.

He architect’s solutions on Cloud as well On-Premises using wide array of platforms / technologies. Having core training and consulting experience, he is involved in delivering Machine Learning, Azure and Blockchain training to corporates like BCG, Microsoft, Intuit, RedHat, VMWare, HCL, GE, Applied Materials, Dell, Infosys, IBM, Schneider, L&T, TCS, Capgemini, Mercedes-Benz, Oracle, HP, Wipro, Colt, Cipla and Mindtree, etc.

He works on projects offering learning and understanding in the fields of Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Big Data Analytics and Data Visualization. He is also a Microsoft Certified Trainer, Microsoft certified solution expert for cloud platform and infrastructure, Microsoft certified solution associate for Machine Learning, Microsoft certified solution developer for azure platform.

He holds following Certifications:

  • Microsoft Certified Trainer (MCT)
  • Microsoft Certified Solutions Expert – Cloud Platform and Infrastructure
  • Microsoft Certified Solutions Associate – Machine Learning
  • Microsoft Certified Solutions Developer – Azure
  • Microsoft Specialist: Developing Microsoft Azure Solutions (70-532)
  • Microsoft Specialist: Implementing Microsoft Azure Infrastructure Solutions (70-533)
  • Microsoft Specialist: Architecting Microsoft Azure Solutions (70-534) (2016)
  • Microsoft Specialist: Architecting Microsoft Azure Solutions (70-535) (2018)
  • Exam 773 – Analyzing Big Data with Microsoft R
  • Exam 774 – Perform Cloud Data Science with Azure Machine Learning
  • Exam 776 – Perform Big Data Engineering on Microsoft Cloud Services
MCT MCSE MCSA Machine Learning
MCSD Azure 532 Developer 533 Infrastructure
534 Architect 535 Architect 773 Big Data with Microsoft R
Perform Cloud Data Science 776 Big Data  Engineering

Other Details


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