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How to deploy Machine Learning models on AWS using Sagemaker

How to deploy Machine Learning models on AWS using Sagemaker

The simplest way to deploy a machine learning model is to create a web service for prediction. In this example, we use the Flask web framework to wrap a simple random forest classifier built with scikit-learn.

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What you'll learn

  • Learn how to use different Built in Sagemaker Algorithms
  • Learn how to deploy an Machine Learning model on AWS using Sagemaker
  • Learn how to use a model Default monitor
  • Learn how to do a Processing Job
  • Learn how to evaluate a deployed Model
  • Learn how to Develop a baseline Dataset
  • Learn how to get predictions from different deployed Models
  • Learn about hyperparameter tuning of an XGBoost model with Sagemaker
  • Learn how to build some medical treatment prediction Models
  • Learn how to address a class imbalance


This course is very hands on Machine Learning with AWS Sagemaker. When you first start this course you will learn how to simply deploy an model to an endpoint. By the end of this course you will be able to hyperparameter tune, use a default model monitor, and more. Do not worry about having experience with Sagemaker I will teach you in depth how to use various the algorithms. As well as many other features on Sagemaker including processing jobs and data capture configuration as well as many more. We will cover both Supervised Learning and Unsupervised Learning on AWS Cloud with Sagemaker. Also one module where we deploy a natural language processing model using Sagemaker. I will also show you how to get predictions from end points and evaluate your machine learning models that are deployed. We will also address many common issues people have getting started with Sagemaker. You will grow from little or no experience to very confident in your new ability to deploy Sagemaker models on AWS. So do not worry if you even have no experience with Sagemaker. The only thing that is required is Intermediate level python and machine learning. With very little to no knowledge of AWS Sagemaker or even AWS in general. There are quizzes in my course. But as long as you pay attention and do the assignments properly you will not have a problem with them at all. You will also learn knowledge of the next steps you will need to do for full production. Yes this course does include AI in medicine however no previous knowledge is necessary to complete the assignments. Also most importantly have fun learning.

View The Best -- > AWS Machine Learning Certification Exam | Complete Guide

  • In order to pass the Machine Learning AWS - Specialty exam, one does need some experience in AWS and machine learning, namely:
  • 1 to 2 years of experience developing, architecting, or running machine learning/deep learning workloads on the AWS Cloud.
  • Understanding and intuition behind basic ML algorithms.

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