Amazon Web Services Inc. continues to expand the reach of its generative artificial intelligence assistant Amazon Q by bringing it to more applications for business users.

At the company’s re:Invent 2024 conference today, AWS announced a new capability for Amazon Q in QuickSight that will help business users perform lengthy scenario analysis to find answers to complex problems quickly. Amazon also announced that Q Developer is now available in SageMaker Canvas, a tool for building large-scale machine learning and artificial intelligence models, using a visual interface without needing code or expertise.

AWS launched Amazon Q earlier this year for developers and business users to act as a generative AI assistant that can help with coding and business tasks. It consists of multiple formats including Amazon Q Developer, which works alongside developers and IT professionals where it acts as a coding assistant within code editors. Business users get access to Amazon Q Business, which can securely use an organization’s enterprise data, helping employees stay prepared and productive.

QuickSight is a cloud-based business intelligence tool that allows business users to analyze data, create visualizations and share insights. It supports various data sources, including databases, data warehouses, software-as-a-service applications and files. Users can create dashboards, reports and charts to visualize data.

“The convergence of business intelligence and generative AI with Amazon Q will continue to unlock new possibilities for our customers, but as these models became more and more powerful, we know we could do more to accelerate data-driven decision-making,” said Dr. Swami Sivasubramanian, vice president of AI and data at AWS. “Today many business users are faced with questions that cannot be answered by a simple Q&A on their data.”

Analysis tasks often lead to complex approaches to develop charts or visualizations from data sources, which can be a laborious amount of knowledge work. This new Amazon Q capability can take in complex questions and analyze multiple data sources simultaneously to suggest an analytical approach to address a business goal.

For example, a business user could ask the AI assistant, “How can I help our store perform as well as the flagship store in Phoenix, AZ?”

Using an agent-based approach, Amazon Q would then automatically analyze the data, present results complete with visualizations in QuickSight and suggest actions. It would do so across the software’s canvas, which would enable the user to make adjustments to the plan, explore different approaches and adapt their ideas.

According to Sivasubramanian, using Q’s new capabilities in QuickSight, business users can perform complex analysis 10 times faster than using spreadsheets.

Each step of the way, Amazon Q remains on hand with a conversational interface allowing the user to ask more questions and get answers about the analysis. If the answer changes the projections or visualizations, the assistant can update the canvas and act as a collaborator alongside the business user to help them get where they’re going faster.

Amazon Q now helps users build machine learning models

Amazon also announced that Q Developer is coming to SageMaker Canvas, a no-code machine learning model-building platform that will make collaborating on building, customizing and deploying new models easier for less technical users.

Through bringing Q Developer to SageMaker Canvas, business users with expert knowledge in their particular industry can quickly build accurate, production-quality machine learning models using natural language interactions.

Q Developer guides users through a conversational interface by breaking down business problems and data using step-by-step guidance for building custom machine-learning models using SageMaker Canvas. It will also clean their data to fix anomalies, build and evaluate their models to recommend the best one to fit their goals and guide them through a workflow.

For example, a user could ask Q, “I want to build a model that will help me predict the number of passengers that will take rideshares across certain days given historical patterns of past usage, weather data, pricing, holidays and events.” Q Developer would then take that plan and analyze the given data to build multiple models to provide an approach.

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