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( Duration: 2 Days )

In SageMaker training course, you will learn 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. You 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.

By attending SageMaker workshop, delegates will learn to:

  • 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

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
The SageMaker class is ideal for:
  • Analysts
  • Developers
  • Data Scientists



Introduction to Machine Learning

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

Introduction to Data Prep and SageMaker

  • Training and Test dataset defined
  • Introduction to SageMaker
  • SageMaker console
  • Launching a Jupyter notebook

Problem formulation and Dataset Preparation

  • Business Challenge: Customer churn
  • Review Customer churn dataset

Data Analysis and Visualization

  • Loading and Visualizing your dataset
  • Cleaning the data

Training and Evaluating a Model

  • Types of Algorithms
  • XGBoost and SageMaker
  • Training the data
  • Hyperparameter tuning with SageMaker
  • Evaluating Model Performance

Automatically Tune a Model

  • Automatic hyperparameter tuning with SageMaker

Deployment / Production Readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling Scaling
  • Configure and Test Autoscaling
  • Check Hyperparameter tuning job
  • AWS Autoscaling

Relative Cost of Errors

  • Cost of various error types
  • Binary Classification cutoff

Amazon SageMaker Architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.