Amazon Introduces SageMaker Canvas. Just Another ML No-Code Platform?

Amazon SageMaker Canvas aims to help business analysts build their own ML systems without dependence on data engineering teams.

Low-code or no-code application platforms have been popular for quite a while now, and companies have gravitated towards their usage due to the benefits they hold. In fact, Gartner says that by 2025, 70% of new applications developed by organisations will use low-code or no-code technologies, up from less than 25% in 2020. 

To cater to this growing demand, a variety of options are available to choose from. Now, tech giant Amazon has gone one step further and introduced another no-code machine learning Amazon SageMaker Canvas at this year’s recently concluded AWS re:Invent. Amazon says that SageMaker Canvas is a new visual, no code capability that allows business analysts to build ML models and gives accurate predictions without writing code or requiring ML expertise.

Built on the capabilities of Amazon SageMaker, Sagemaker Canvas will be a good choice among the different no-code ML platforms. Let’s look at why it will be in demand.

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Less dependence on data engineering teams; build ML systems yourself

Building and deploying machine learning models is not an easy task. It needs coding and AI-specific knowledge, which may not be available easily in every organisation. If such capabilities are not present in the company, they may have to hire experts from outside, which may prove to be a costly affair. 

Amazon points out that it does not want members of an organisation such as data analysts and business analysts to depend on other data science and data engineering teams for creating ML machine learning (ML) systems. Through Amazon SageMaker Canvas, Amazon wants to give business analysts that deal with huge amounts of data every day the capability to build and use prediction systems based on the data without having to learn about hundreds of algorithms.

Easy and intuitive

Sagemaker Canvas is easy to handle. It has an intuitive user interface that allows one to browse and access disparate data sources on-premises or in the cloud. It can also combine datasets with just one click, then train accurate models and generate new predictions once new data is available.

Builds on the popular Amazon SageMaker

Amazon SageMaker has become one of the most popular no-code ML platforms, and SageMaker Canvas builds on this popularity. It uses the same technology as Amazon SageMaker to automatically clean and combine the data, creating hundreds of models. Then, it selects the best performing one and generates new individual or batch predictions. 

The Amazon SageMaker Studio integration lets users share the model easily with other data scientists in a team. It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. The platform also comes with built-in cybersecurity and compliance features which some other platforms lack.


With so many options available to go for building, training, and deploying machine learning models, the financial considerations of these platforms need analysis. In a post last year, Amazon showed how the Total Cost of Ownership (TCO) of SageMaker over a three-year horizon is 54% lower compared to other cloud-based ML options such as self-managed Amazon EC2 and AWS managed Amazon EKS.

Comes with improved capabilities

SageMaker had announced certain improvements last year like Data Wrangler, Feature Store, and Pipelines. This new release, using the same technology as SageMaker, builds on these capabilities as well. Amazon SageMaker Data Wrangler allows customers to choose the data they want from their various data stores and provides them with the option of importing it with a single click. Amazon SageMaker Feature Store gives a new repository that makes it easy to store, update, retrieve, and share machine learning features for training and inference. The SageMaker Pipeline provides easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning.

Sreejani Bhattacharyya
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good. Reach me at

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