This tutorial walks you through the process of creating a machine learning (ML) solution on Amazon Web Services (AWS). You’ll learn how to use Amazon SageMaker to train and deploy a model that predicts whether a movie review is positive or negative.
Checkout this video:
Introduction to AWS for Machine Learning
This tutorial introduces you to Amazon Web Services (AWS) for machine learning. You learn how to use various AWS services to build a machine learning (ML) solution. This tutorial uses illegal activity detection as an example, but you can apply the steps to other situations.
AWS machine learning services provide the ability to build, train, and deploy ML models. These services are scalable and provide high performance by using powerful hardware and low-latency networks. With these capabilities, you can process large amounts of data quickly and train sophisticated models.
The first part of this tutorial walks through the process of building an ML model using Amazon SageMaker, an AWS service for building, training, and deploying ML models. The second part introduces Amazon EMR, which you can use to preprocess data before training your model or to post-process results after your model is deployed.
Setting up your AWS account
If you don’t have an AWS account yet, you’ll need to create one. You can sign up for a free trial account here.
Once you have an account, log in to the AWS Management Console and select the “IAM” service from the “Security, Identity & Compliance” section.
On the left hand side of the IAM console, select “Users” and then click on the “Add User” button.
Getting started with Amazon SageMaker
This tutorial walks you through the process of creating and training a machine learning (ML) model on an Amazon SageMaker Jupyter Notebook. In it, you learn how to:
-Set up your working environment on an Amazon SageMaker notebook instance
-Download and prepare training data
-Create an Amazon SageMaker training job to train a model
-Deploy the trained model to an Amazon SageMaker endpoint
-Test the deployed model
Building your first machine learning model
In this tutorial, you’ll learn how to build a machine learning model using Amazon SageMaker. We’ll use the linear learner algorithm to train the model. Linear learner is a binary classification algorithm that supports both multiclass classification and regression.
You’ll use the Amazon SageMaker Python SDK to build and train your machine learning model. The SDK provides convenient interfaces for many Amazon SageMaker features, including downloading and uploading data, creating and deploying training jobs, tuning models, and compiling models for inference.
If you’re new to Amazon SageMaker, we recommend that you review the following tutorials before starting this one:
– Setting Up Your Development Environment
– Getting Started with Amazon SageMaker
Training and deploying your machine learning model
Now that you have completed the data pre-processing and exploratory data analysis, it is time to train and deploy your machine learning model. In this tutorial, you will use Amazon Web Services (AWS) to set up an environment for machine learning. You will learn how to:
– Set up an Amazon Elastic Compute Cloud (EC2) instance
– Configure the EC2 instance for machine learning
– Train a machine learning model on the EC2 instance
– Deploy the trained model on the EC2 instance
By the end of this tutorial, you will have a deployed machine learning model that you can use to make predictions on new data.
Evaluating your machine learning model
In machine learning, there is a distinction between accuracy and precision. Accuracy is the percentage of your predictions that are correct, while precision is the percentage of positive predictions that are actually correct. In other words, accuracy measures how often you are right, while precision measures how often you are right when you predict positive.
There are a few different ways to evaluate your machine learning model. A common way is to use a confusion matrix, which shows the number of correct and incorrect predictions for each class. Another way is to use precision and recall, which are defined as follows:
Precision = true positive / (true positive + false positive)
Recall = true positive / (true positive + false negative)
F1 score is a measure that combines precision and recall. It is defined as follows:
F1 score = 2 * (precision * recall) / (precision + recall)
Amazon SageMaker Studio: A hosted development environment for machine learning
Amazon SageMaker Studio is a fully integrated development environment (IDE) formachine learning. It provides a single, web-based UI where you can perform all ML workflows. Studio gives you complete access to all of the tools and resources that you need to build, train, and deploy models. These include notebooks, an experimentation UI, a model registry, an automated model builder, and one-click deployments.
Amazon SageMaker Ground Truth: Labeling data for machine learning
In this tutorial, you learn how to use Amazon SageMaker Ground Truth to generate highly accurate training data for your machine learning models. Amazon SageMaker Ground Truth makes it easy to build highly accurate training datasets for computer vision and natural language processing (NLP) by managing the entire labeling workflow. With Ground Truth, you can use either automated data labeling or have humans label your data. Automated data labeling is less expensive and generates high-quality labels quickly. You can also use a mix of both automated and human labeling to get the most cost-effective and high-quality training dataset for your machine learning models.
Amazon SageMaker Neo: Optimizing machine learning models for performance
Amazon SageMaker Neo is a machine learning model optimization service that helps you improve the performance of your models by compiling them to run more efficiently on a specific target platform. In this tutorial, you will learn how to use SageMaker Neo to optimize a pre-trained image classification model for performance on an Amazon EC2 instance with an Arm-based processor.
Amazon SageMaker Autopilot: Automated machine learning
In this tutorial, you use Amazon SageMaker Autopilot to automatically discover the best variants for a machine learning (ML) model by using several built-in algorithms to train and evaluate many models in parallel. You don’t need any prior ML experience to use Autopilot. This is an end-to-end example that shows you how to use Autopilot on Amazon SageMaker to prepare data, automatically discover models, select the one that performs the best, and deploy it as an Amazon SageMaker endpoint.
Keyword: AWS for Machine Learning Tutorial