SageMaker Canvas has empowered SuccessKPI to fine-tune Foundation Models using historical question-answer pairs, enabling more accurate assessment of agent-customer conversations without extensive (time consuming, laborious, costly) manual review. The fine-tuning capabilities leveraging AutoML allow SuccessKPI to optimize models and provide access to crucial performance metrics. By leveraging Canvas, SuccessKPI has significantly enhanced operational efficiency, automating conversation scoring, and improving the overall quality of contact center interactions.

Through AWS’s no-code ML services such as SageMaker Canvas and SageMaker Data Wrangler, we at Deloitte Consulting have unlocked new efficiencies, enhancing the speed of development and deployment productivity by 30–40% across our client-facing and internal projects.

One of the challenges that our engineers face is managing customer call resources during peak seasons to ensure the optimal number of customer service personnel are hired to handle the influx of inquiries, historical analysis of call center data containing call volume, wait time, date, and other relevant metrics is time consuming. Our teams are leveraging the new data preparation and customization capabilities in SageMaker Canvas to train models on company data to identify patterns and trends that impact call volume during peak hours. It was extremely easy for us to build ML models using our own data, and we look forward to increasing the use of foundational models—without writing any code—through Canvas.

One of the biggest challenge for Aura is to extract meaningful insights from a vast pool of unstructured professional data, by employing Large Language Models via Amazon SageMaker Canvas, we've automated the data extraction process, transforming how companies assess workforce competencies and organizational structures. This approach not only helped us scale data analysis, but also bypassed the limitations of traditional data analysis methods like keyword matching. Using SageMaker Canvas' new data preparation and LLM capabilities, Aura is able to quantitatively score and benchmark companies on the effectiveness of their organization structure, skills of the workforce, and performance in terms of financial results.

Using Amazon SageMaker Canvas is simple, and the interface is user friendly. Even a business analyst like me can analyze data and get insights using machine learning.

Our clients are often hindered by the quality and integration of their own data—a challenge that is ubiquitous among hospitals across the US. In addition, accumulated decades of inconsistent quality assurance practices and fragmented data processes have resulted in the arduous task of cleansing, validating, and integrating billions of rows of erroneous data. To help them tackle this challenge, our analytics teams use Amazon SageMaker Canvas to derive, cleanse, and standardize data coming from an unlimited number of sources from healthcare providers in the US, allowing them to easily build custom text classification models—all without writing a single line of code. With SageMaker Canvas, we offer our clients validated and standardized product descriptions and classifications for ERP data, verified clinical classifications for procedural utilization data, and data-driven recommendations designed to optimize the quality of patient care, all while reducing costs and saving time. By equipping our clients with trustworthy data and cutting-edge analytics, we empower them to look ahead and build a brighter future for healthcare, instead of constantly playing catch-up with the present.

The core of our data science strategy at Siemens Energy is to bring the power of machine learning to all business users by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert. This enables us to increase the speed of innovation and digitalization of our energy solutions such as Dispatch Optimizer and Diagnostic services. We found Amazon SageMaker Canvas a great addition to the Siemens Energy machine learning toolkit, because it allows business users to perform experiments while also sharing and collaborating with data science teams. The collaboration is important because it helps us productionalize more ML models and ensure all models adhere to our quality standards and policies.

Our business analysts are data savvy, and we needed the ability to let them create predictive models. Equally important, however, was to ensure that our data science team had visibility into the models built so that they can support and productionize as needed. We foresee Amazon SageMaker Canvas empowering our business users and process engineers to start working on data science problems that were previously owned by the data science team. The intuitive user interface and easy-to-navigate options of Amazon SageMaker Canvas allow business users to import a variety of data, minimize the need to manually clean up data, and apply a variety of algorithms to find the model that best fits the data with a few clicks. The code and data can easily be sent to the data science team through Amazon SageMaker Studio, allowing them to integrate models into their model management system and see a full picture of models enterprise wide.

The use of Artificial Intelligence as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout the value chain, enabling it to generate added value for customers, products, employees, and processes. In the past few years, we have industrialized many top BMW Group use cases measured by business value impact. We believe Amazon SageMaker Canvas can add a boost to our AI/ML, scaling across the BMW Group. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production.