Bringing Machine Learning to the Masses: AWS Announces sagemaker Canvas

Bringing Machine Learning to the Masses: AWS Announces Sagemaker Canvas

Summary Bullets

Will Stofega, Principal Analyst, Advanced Analytics

• SageMaker Canvas looks to democratize access to machine learning (ML).

• Access to analytic tools will generate a better understanding of company performance.

When discussing the future of artificial intelligence (AI) and ML, there is often an emphasis on advanced technical capabilities that will help realize futuristic visions such as self-driving cars. Given the competitive advantages at stake, it is no surprise that demand for skilled AI and ML specialists has quickly outstripped supply.

During his AWS re:Invent 2021 presentation, Bratin Saha, Vice President, Machine Learning, noted that AI and ML is the fastest-growing job category, posting a 74% growth rate in the U.S. over the past four years (per LinkedIn’s 2020 Emerging Jobs Report). Not surprisingly, this has led to a shortage of qualified AI and ML specialists. According to Saha, AWS customers are looking for solutions that empower more employees to do ML. This need for access by non-technical staff has led AWS to develop SageMaker Canvas.

Unveiled at AWS re:Invent 2021, Canvas allows workers lacking ML skills to build models that generate actionable insights from their data sets without writing a single line of code. Utilizing a visual interface, Canvas users can browse and access disparate data sources in the cloud, including Amazon S3, Redshift, or on-premises, combine the datasets, train models, and generate predictions.

Canvas utilizes SageMaker to clean, correct, and combine data automatically, then creates models and selects the most accurate by assigning the results an accuracy score. It supports multiple problem types such as binary, multi-class, numerical regression, and time series forecasting. During a presentation at AWS re:Invent 2021 highlighting the capabilities of Canvas, Kimberly Madia, AWS Sr. Manager of Product Marketing, demonstrated how Canvas can help generate confidence in the results. Using a feature known as Analyze View, Canvas can explain the prediction to those using the model, providing answers to questions while allowing additional model review for potential errors.

One of the most challenging chores in any business is to develop accurate predictions or forecasts regarding sales and revenue. Companies spend countless hours gathering data, building models, and then discussing and revising models typically built using Microsoft Excel. Tools such as Canvas offer a more efficient process to business managers as they look to compete in an environment affected by an increasingly diverse variety of factors. Tools such as ML should not be locked away from employees that are not part of the technology staff. History has shown that the democratization of technology (think of the journey from centralized computing to the PC) has unleashed innovation and increased productivity.

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