How to deploy Machine Learning models on AWS using Sagemaker

How to deploy Machine Learning models on AWS using Sagemaker, available at $39.99, has an average rating of 3.86, with 9 lectures, 8 quizzes, based on 7 reviews, and has 65 subscribers.

You will learn about 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 ideal for individuals who are Someone in the industry or student who wants to learn to use AWS Sagemaker. or Someone who wants to learn more features of AWS Sagemaker or Someone who wants to strengthen thier machine learning skills. It is particularly useful for Someone in the industry or student who wants to learn to use AWS Sagemaker. or Someone who wants to learn more features of AWS Sagemaker or Someone who wants to strengthen thier machine learning skills.

Enroll now: How to deploy Machine Learning models on AWS using Sagemaker

Summary

Title: How to deploy Machine Learning models on AWS using Sagemaker

Price: $39.99

Average Rating: 3.86

Number of Lectures: 9

Number of Quizzes: 8

Number of Published Lectures: 9

Number of Published Quizzes: 8

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Someone in the industry or student who wants to learn to use AWS Sagemaker.

Someone who wants to learn more features of AWS Sagemaker

Someone who wants to strengthen thier machine learning skills.

Target Audiences

Someone in the industry or student who wants to learn to use AWS Sagemaker.

Someone who wants to learn more features of AWS Sagemaker

Someone who wants to strengthen thier machine learning skills.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Linear Learner for Regression

Lecture 2: Linear Learner Multi Class Classification with hyperparameter tuning

Lecture 3: XGBoost Multi Class Classification

Chapter 2: Some more various built-in algorithms

Lecture 1: Deploy a Kmeans model on AWS

Lecture 2: Deploy an IPinsights Model to an endpoint and predict the whole darknet data set

Lecture 3: How to deploy an XGBoost reg then tune the hyperparameters to address inaccuracy

Chapter 3: Medical uses of Machine Learning using AWS Sagemaker and using a default model

Lecture 1: Use SeqtoSea model which is a form of Google Translate

Lecture 2: Medical treatment prediction model with a default model monitor

Lecture 3: Diabetic Prediction model with hyperparameter tuning to address bias and more

Instructors

Marshall Trumbull
Machine Learning Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 0 votes

5 stars: 4 votes

Frequently Asked Questions

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