Seamless ML Model Development and Deployment with Amazon SageMaker Canvas

Introduction

Amazon SageMaker Canvas is a graphical user interface (GUI) tool provided by Amazon Web Services (AWS) for creating, training, and deploying machine learning (ML) models using Amazon SageMaker. It provides a visual interface for building ML workflows, including data preprocessing, feature engineering, and model training.

Pre-requisites of Amazon SageMaker Canvas

Create a domain in Amazon SageMaker:

• Go to Amazon SageMaker
• Click on domains
• Create a domain. There are 2 ways to create a domain:
• Quick setup
• Standard setup
• After the creation of the domain, click on the domain and click on launch as canvas.

Step-by-Step Guide

Step 1 – Importing data in Amazon SageMaker Canvas

In Canvas, the user can see all the data set under the dataset tab.

• For importing the dataset, click on the import button at the top right corner

For importing from local: select upload–> select files from computer–>click import data

For importing from Amazon S3: select S3–> select bucket–> file–>import data

Step 2 – Build a model

Once the dataset is in the ready state. Select the dataset and click on Create the model and give a name to model

Under the build tab, give the target column name. Based on the target column, two types of models can be created:

• In machine learning, categorical prediction is referred to as classification.

1. 2 category prediction in Amazon Sagemaker canvas is referred for binary classification
2. 3+ category prediction in Amazon Sagemaker canvas is referred for multi-class classification

• Numerical prediction is referred to as regression

For performing exploratory data analysis, click on data.

After performing exploratory data analysis, the columns which are not needed can be removed by unchecking that particular column, and this would come under the model recipe.

Similarly, if we need to apply a formula on different columns, this can be done by clicking on the function panel, applying the formula, and giving the column name. This will also get added under the model recipe.

Before building the model, validate the data as this helps to find any issue in the dataset and fix it. After that, to build a model, click on quick build or standard build.

1. Quick build: Construction time is only 15 to 20 minutes, but the model is not precise. Moreover, your input dataset’s maximum number of rows is 50,000.
2. Standard Build: This process takes 90 to 100 minutes. Its precision is higher than that of a quick build. This user can share the model with the data scientist and review it.

Step 3 – Evaluate model

• Under analyze tab, we can see model performance and scoring. In advanced metrics, we can see a confusion matrix
• Users can also see the feature importance of each column under overview and see the graph with respect to the target column.

Here model predicts 98.93% of time Converted correctly.

Step 4 – Make predictions

Under predict for batch predictions user can upload the validation dataset and see the predicted label and probability

Conclusion

Amazon SageMaker Canvas is a powerful and user-friendly tool for building and deploying machine learning models. Its drag-and-drop interface makes it accessible to users with varying technical expertise and supports various ML models and tasks.

By simplifying the process of building and deploying ML models, Amazon SageMaker Canvas can help businesses and data scientists save time and resources and enable them to experiment with different models and workflows more quickly. Additionally, its integration with other AWS services provides even more flexibility and customization options.

Overall, Amazon SageMaker Canvas is a valuable addition to the suite of tools and services offered by AWS and can be a great asset for any organization looking to harness the power of machine learning.

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