An Introduction to AWS SageMaker

An Introduction to AWS SageMaker

Amazon Web Services, or AWS, is Amazon’s cloud computing service consisting of a mix of IaaS, PaaS, and SaaS infrastructure.

Amazon Web Services consists of many organizational tool services such as computing power, database storage, to content delivery services. AWS services include servers, networking, remote computing, email, mobile development, and security.

The AWS cloud platform is known as a service that offers flexible, scalable, easy-to-use, and cost-effective cloud computing solutions. Amazon Web Services was launched in 2006 from an internal infrastructure built to handle Amazon’s enterprise online retail operations.

Amazon was one of the first companies to introduce a pay-as-you-go cloud computing model that can be used as needed. AWS offers many different services and solutions for enterprises and software developers that can be deployed in data centers in 190 countries.

This cloud platform is also used in many industries, Golden friends! From government agencies, educational institutions, and non-profit organizations to private organizations

What is AWS SageMaker?

Amazon SageMaker is a fully-managed service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning (ML) models.

It provides a wide range of tools and capabilities, such as pre-built algorithms, Jupyter notebooks, and built-in model monitoring, to make it easy to build, train, and deploy ML models.

Additionally, it integrates with other AWS services such as Amazon S3 and Amazon EC2, allowing for easy data storage and computing resources.

AWS SageMaker Advantages

Amazon SageMaker is a powerful tool for building and deploying machine learning models. Some of the key benefits of using SageMaker include:

1. Increase Productivity

SageMaker simplifies the process of building and deploying ML models, allowing developers and data scientists to focus on creating high-quality models rather than spending time on infrastructure and other operational tasks.

2. Easy Compute Management

SageMaker makes it easy to create and manage compute instances, which are essential for training and deploying ML models. This means you can spin up instances quickly and with minimal effort.

3. Automatic Model Building and Deployment

SageMaker can automatically create, deploy, and train models based on your data, providing you with complete visibility into the process. This makes it much easier to build high-quality models.

4. Cost-Saving

SageMaker can help you reduce the cost of building and deploying ML models by up to 70%, making it more accessible to a wider range of users.

5. Data Labelling Made Easy

SageMaker provides a range of tools for data labeling, which can help you quickly and easily prepare your data for model training.

6. Centralized ML Components

SageMaker allows you to store all your ML components in one place, making it easy to keep track of your models and data.

7. Scalability and Speed

SageMaker is highly scalable, allowing you to easily train models on large data sets. Additionally, it allows you to train models faster, which can help you get your models to market more quickly.

Machine Learning AWS SageMaker

Let’s examine the idea of using AWS SageMaker for Machine Learning, and learn how to construct, test, optimize, and deploy a model.


AWS SageMaker offers over 15 commonly used ML algorithms for training, allowing users to select the appropriate server size for their notebook instance.

Users can utilize the notebook interface to write code for creating model training jobs, as well as choose and optimize algorithms such as K-means, Linear regression, and Logistic regression.

This platform enables developers to tailor Machine Learning instances with the use of Jupyter notebook.

Test and Tune

To begin, set up and import the necessary libraries. Define and manage environment variables for training the model using Amazon SageMaker.

The platform allows for hyperparameter tuning by incorporating a combination of algorithm parameters. SageMaker utilizes Amazon S3 for data storage, as it is secure, and ECR for managing Docker containers, which is highly scalable.

The training data is divided and stored in Amazon S3, while the training algorithm code is saved in ECR. SageMaker then creates a cluster for the input data, trains it and saves the results in Amazon S3.


After tuning is completed, models can be deployed to SageMaker endpoints for real-time predictions. Evaluate the model to determine if it meets your business objectives.

How to Train A Model with AWS SageMaker

In SageMaker, model training is performed on machine learning compute instances. When a user trains a model in Amazon SageMaker, they create a training job, which includes:

  • An S3 bucket within the compute instance that contains the URL of the Amazon S3 bucket where the training data is stored
  • AWS SageMaker on ML instance: Compute resources or Machine Learning compute instances
  • An S3 bucket outside the compute instance where the output will be stored
  • The path of the AWS Elastic Container Registry where the inference code image is saved

The input data is retrieved from the specified Amazon S3 bucket. Amazon SageMaker then launches the ML compute instances and trains the model with the training code and dataset.

The output and model artifacts are stored in the AWS S3 bucket. If the training code fails, the helper code performs the remaining task.

The inference code is composed of multiple linear sequence containers that process the request for inferences on data. EC2 container registry is a storage registry that helps users to save, monitor, and deploy container images.

Once the data is trained, the output is stored in the specified Amazon S3 bucket. To prevent the algorithm from being deleted, save the data in Amazon SageMaker critical system processes on your ML compute instances.

How to Validate a Model With SageMaker?

You can evaluate your model using offline or historical data:

1. Offline Testing

In offline testing, the model is tested using historical data. This means that the model is evaluated using data that it has not seen before during its training phase.

This type of testing is typically done using Jupyter notebook in Amazon SageMaker. By using Jupyter notebook, developers can send requests to the model using the historical data and evaluate its performance.

This method allows developers to test the model’s accuracy and reliability before deploying it in a live environment.

2. Online Testing with Live Data

Online testing with live data is the process of testing the model’s performance in real-time.

This type of testing involves deploying multiple models into the endpoint of Amazon SageMaker and directing live traffic to the model for validation.

This method allows developers to test the model’s performance under real-world conditions and make adjustments if necessary.

3. Validating Using a “Holdout Set”

In this method, a part of the data is set aside, which is called a “holdout set”. This holdout set is not used during the training phase of the model.

Instead, it is used to evaluate the model’s performance after it has been trained. The model is trained with the remaining input data and is then evaluated using the holdout set.

This method allows developers to evaluate the model’s ability to generalize the data based on what it learned during the training phase.

4. K-fold Validation

In K-fold validation, the input data is split into two parts. One part is called k, which is the validation data for testing the model, and the other part is k − 1 which is used as training data.

The model is trained and evaluated k times, each time using a different portion of the data as the validation set. This method allows developers to train the model with different subsets of data and evaluate its performance.

This method provides a more robust evaluation of the model’s performance as it takes into account the variability of the input data.

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