{"id":829,"date":"2021-12-21T07:38:27","date_gmt":"2021-12-21T07:38:27","guid":{"rendered":"https:\/\/www.workfall.com\/learning\/blog\/?p=829"},"modified":"2025-08-20T10:01:12","modified_gmt":"2025-08-20T10:01:12","slug":"how-to-build-ml-models-to-generate-accurate-predictions-without-writing-code-using-amazon-sagemaker-canvas","status":"publish","type":"post","link":"https:\/\/learning.workfall.com\/learning\/blog\/how-to-build-ml-models-to-generate-accurate-predictions-without-writing-code-using-amazon-sagemaker-canvas\/","title":{"rendered":"How to build ML models to generate accurate predictions without writing code using Amazon SageMaker Canvas?"},"content":{"rendered":"Reading Time: <\/span> 11<\/span> minutes<\/span><\/span>\n

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As a corporation deals with challenges and data on a regular basis, the capacity to design systems that can forecast business outcomes becomes critical. By automating sluggish procedures and incorporating intelligence in your IT systems, you can solve problems and move more quickly.<\/p>\n\n\n\n

But how can you ensure that all teams and individual decision-makers within the company have the authority to build these ML systems at scale without relying on other data science and data engineering teams? Amazon SageMaker Canvas is the answer, one of the most noteworthy announcements at re:Invent 2021. This launch simplifies AI creation by eliminating the requirement for users to write any code. It also doesn’t need a thorough understanding of machine learning.<\/p>\n\n\n\n

Amazon SageMaker Canvas uses the same technology as Amazon SageMaker to automate the process of cleaning and combining the code, creating hundreds of models, selecting the one with the best performance, and finally generating new individual or batch predictions. The supported problem types include binary classification, numerical regression, multi-class classification, and time series forecasting. Such problem types help the users to deal with business-critical use cases like churn reduction, fraud detection, inventory optimization, etc. without the need of writing huge chunks of code. <\/p>\n\n\n\n

\"Amazon<\/figure>\n\n\n\n

Using Amazon SageMaker canvas reduces the hardware costs and maintains the underlying infrastructure cost to the bare minimum. Amazon SageMaker Canvas comes with in-built cybersecurity flexibility and compliance features that eliminate the need to have it implemented from scratch. In this blog, we will have a look at how we can build machine learning models to generate the best accurate predictions without writing code using Amazon SageMaker.<\/p>\n\n\n\n

Hands-on<\/h2>\n\n\n\n
\"Amazon<\/figure>\n\n\n\n

In this hands-on, we will see how we can build ML models to generate accurate predictions without writing code using Amazon SageMaker Canvas.<\/p>\n\n\n\n

Navigate to the AWS dashboard, we will first navigate to the Amazon SageMaker console and check if the selected region supports the canvas domain. If not, we\u2019ll change the selected region. Post that, we will navigate to the Canvas option and set up a new domain.<\/p>\n\n\n\n

We will create a new role for the newly created domain and select a VPC and a subnet for the same domain. Post creation of a domain, we will launch the application using the Canvas option. We will then have a look at the various steps required to build the model and begin with predictions. Navigating to the S3 bucket, we will add a dataset inside the bucket.<\/p>\n\n\n\n

We will then alter the CORS policy for the same bucket. On the canvas dashboard, we will then navigate to the dataset section and select the dataset uploaded inside the S3 bucket. Once the dataset is imported, we will navigate to the Models section and create a new model. Creating a new model, we will select the dataset for which we wish to perform predictions.<\/p>\n\n\n\n

We will then navigate to the Build step of the model creation process. Selecting a target column, we will preview the model to have a rough look at the analysis and the value distribution for the various columns in the dataset. Post that, we will create a Model via the Quick Build option.<\/p>\n\n\n\n

Post the model creation, we will navigate to the analyze tab and have a look at the analysis output as well as the various graphs plotted in terms of value distribution for various columns in the dataset. Then we will finally have a look at the Scoring graph and make the dataset ready for predictions.<\/p>\n\n\n\n

To implement the same, we will do the following:<\/strong><\/p>\n\n\n\n