Learn AWS Machine Learning with Amazon SageMaker

Learn AWS Machine Learning with Amazon SageMaker

Table of Contents

1. Introduction
2. What is SageMaker?
3. SageMaker Studio, Lab, and Canvas
4. Getting Started with Studio Lab
5. Creating a Notebook in Studio Lab
6. Training a Model using Linear Regression
7. Testing the Model
8. Introduction to SageMaker Canvas
9. Creating a SageMaker Domain
10. Building a Model in SageMaker Canvas
11. Visualizing Data in SageMaker Canvas
12. Quick Build and Standard Build
13. Predicting with SageMaker Canvas
14. Introduction to SageMaker Studio
15. Using Pre-Built and Automated Solutions in Studio
16. Creating an AutoML Experiment in Studio
17. Deploying and Predicting with AutoML Model
18. Shutting Down Resources in Studio

Article

Introduction

In this tutorial, we will explore Amazon SageMaker and its various features. SageMaker is a fully managed machine learning service provided by AWS. It offers three main ways to work with machine learning: Studio, Lab, and Canvas. Studio Lab is a free service that doesn't require an AWS account, while both Canvas and Studio require an AWS account. Canvas provides a visual interface for no-code machine learning, while Studio is a fully-featured IDE for machine learning.

What is SageMaker?

Amazon SageMaker is a fully managed machine learning service offered by AWS. It provides a complete toolkit for building, training, and deploying machine learning models. With SageMaker, You can easily build, train, and deploy models without the need for extensive infrastructure setup or management. It offers integration with popular ML frameworks like TensorFlow and PyTorch, making it easy to leverage existing code and models.

SageMaker Studio, Lab, and Canvas

SageMaker offers three main ways to work with machine learning: Studio, Lab, and Canvas. Studio is a fully-featured integrated development environment (IDE) for machine learning, providing a comprehensive set of tools for building, training, and deploying models. Lab is a free service that allows you to experiment and learn with machine learning without the need for an AWS account. Canvas, on the other HAND, is a visual interface for building machine learning models without any code.

Getting Started with Studio Lab

To get started with Studio Lab, you can access it directly using the URL "studiolab.chmaker.AWS". You will need to request an account, which is separate from your AWS account. Once your request is approved, you can sign in and choose a compute Type (CPU or GPU) to start your runtime. After the runtime is ready, you can open your project and start working with the Studio Lab's Jupyter notebooks environment.

Creating a Notebook in Studio Lab

In Studio Lab, you can Create a new notebook with a Python kernel to write a simple machine learning program. You can rename the notebook and install any necessary libraries, such as scikit-learn. You can also generate training data using a simple for loop and define a target Based on a simple equation. Once the data is generated, you can train the model using linear regression and test its performance.

Introduction to SageMaker Canvas

SageMaker Canvas is a visual interface for building machine learning models without any code. It provides a drag-and-drop interface where you can create and connect various nodes to design your model. Before using Canvas, you need to create a SageMaker domain and choose a setup option. You can then create a model and choose the target column for prediction. Canvas also allows you to Visualize your data and filter it based on specific criteria.

Building a Model in SageMaker Canvas

In SageMaker Canvas, you can choose between quick build and standard build options for building your model. The quick build option prioritizes speed over accuracy, while the standard build option focuses on accuracy. After selecting the build option, you can define the target column and explore the value distribution. You can also use the data visualizer to visualize your data in a matrix or graphical format.

Predicting with SageMaker Canvas

Once the model is built in SageMaker Canvas, you can use it for making predictions. SageMaker allows you to predict using both batch prediction and single prediction. Batch prediction is suitable for handling large datasets, while single prediction is useful for making predictions on individual data points. You can change the column values in the prediction interface to observe how they impact the predicted outcome.

Introduction to SageMaker Studio

SageMaker Studio is a fully-featured integrated development environment (IDE) for machine learning. It provides a wide range of tools and features to streamline the entire machine learning workflow. In Studio, you can create notebooks using the open launcher and access pre-built and automated solutions for building models. One such solution is AutoML, which automatically builds, trains, and tunes the best machine learning models based on your data.

Shutting Down Resources in Studio

Once you have completed your work in Studio, it's important to shut down all the resources to avoid unnecessary costs. In the SageMaker dashboard, you can select the shutdown option and choose to shut down all resources. You can also delete any S3 buckets and data files if you no longer need them. It's crucial to manage your resources efficiently to optimize costs and maintain a clean environment.

Highlights

• Amazon SageMaker is a fully managed machine learning service offered by AWS.
• SageMaker provides three main ways to work with machine learning: Studio, Lab, and Canvas.
• Studio is a fully-featured IDE, Lab is a free service, and Canvas is a visual interface for building machine learning models.
• Studio Lab allows you to create notebooks and write machine learning programs using Python.
• SageMaker Canvas offers a drag-and-drop interface for building machine learning models without coding.
• SageMaker Studio provides a comprehensive set of tools for building, training, and deploying models. It also offers pre-built and automated solutions like AutoML.
• It's important to shut down resources and manage them efficiently to optimize costs.

FAQ

Q: What is the difference between SageMaker Studio and SageMaker Lab? A: SageMaker Studio is a fully-featured integrated development environment (IDE) for machine learning, while Lab is a free service that allows you to experiment and learn with machine learning without the need for an AWS account. Studio offers more advanced features and functionalities compared to Lab.

Q: Can I access Studio Lab directly without going through the AWS console? A: Yes, you can access Studio Lab directly using the URL "studiolab.chmaker.AWS". However, you need to request an account and get it approved before accessing Studio Lab.

Q: What is AutoML in SageMaker Studio? A: AutoML is a pre-built and automated solution in SageMaker Studio that automatically builds, trains, and tunes the best machine learning models based on your data. It simplifies the model-building process and helps in quickly getting quality results.

Q: How can I optimize my costs in SageMaker? A: To optimize costs, make sure to shut down resources when not in use. You can use the shutdown option in the SageMaker dashboard to shut down all resources. Additionally, delete any unnecessary S3 buckets and data files to save storage costs.

Q: Can I share quick build models in SageMaker Canvas? A: No, quick build models cannot be shared in SageMaker Canvas. You need to choose the standard build option if you want to share your models.

Q: What happens if my machine learning dataset is too small? A: If your dataset is too small, it may not be sufficient to build accurate machine learning models. In such cases, you may need to gather more data or consider using techniques like data augmentation or transfer learning to improve model performance.