Amazon launched SageMaker Canvas yesterday during a keynote talk at its re:Invent 2021 conference, which allows users to develop machine learning models without writing any code. This will enable AWS users to execute a machine learning process with a point-and-click user interface using SageMaker Canvas to produce predictions and publish the findings.
The keynote began with the narration of how AWS’s EC2 instances were the first true game-changer for the corporation and its upcoming plans focusing on Arm-based Gravitron processors. At the event, Amazon unveiled its third-generation self-developed server processor Graviton3, IoT TwinMaker, self-developed cloud AI training chip Trn1, and AWS Mainframe Modernization. The event also announced the preview of a new managed service called AWS Private 5G that helps enterprises set up and scale private 5G mobile networks in their facilities in days instead of months.
With emphasis to help users lacking extensive artificial intelligence knowledge or training, SageMaker Canvas supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. The perk of having such extensive support is that you can solve business-critical use cases like fraud detection, churn reduction, and inventory optimization without creating a single line of code.
According to AWS CEO Adam Selipsky, Canvas allows customers to browse and access petabytes of data from both cloud and on-premises data sources, such as Amazon S3, Redshift databases, and local files. Using automated machine learning technology, Canvas creates models, and users can then explain and analyze these models, as well as share them with others to contribute and deepen findings. They can also integrate data sets with a single click, train reliable models, and then create updated predictions as new data is available.
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SageMaker Canvas automates the most time-consuming aspects of data preparation. The application aids in the detection of errors such as missing spreadsheet fields and automates the tedious effort required in merging data from several sources.
After curating the training dataset, businesses can begin creating their AI model where SageMaker Canvas could determine the accuracy of a neural network before it is launched. They can analyze the estimate and, if necessary, change their datasets to increase accuracy.
SageMaker Canvas examines hundreds of AI models and selects the most effective one for the processing task the user wants to automate. With a few clicks, workers may train the neural network on their datasets.
SageMaker Canvas employs Amazon SageMaker’s sophisticated AutoML technology, which automatically trains and constructs models depending on your dataset. SageMaker Canvas can then use this information to choose the optimal model for your dataset and deliver single or bulk predictions. Business analysts may easily exchange models with data scientists using SageMaker Canvas, which is connected with SageMaker Studio.
Building a machine learning model often necessitates not just coding expertise but also experience with AI-specific development tools like TensorFlow. Enterprise AI initiatives might be difficult in numerous ways due to the demand for specific expertise.
If a corporation lacks AI knowledge in-house, it may need to outsource experts to help with machine learning initiatives. Meanwhile, companies that already have the requisite technological know-how could face difficulties too. When it comes to launching AI or machine learning initiatives, business users frequently need developer support, which slows down the rate at which businesses can implement AI. Hence, no code solutions are being increasingly adopted across industries to boost machine learning-powered AI tools’ use and deployment. According to Gartner, by 2024, no-code development will account for 80% of all ICT goods and services.
Amazon SageMaker Canvas is now available in the US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Ireland) AWS Regions.
To read more about SageMaker Canvas, visit here.
