Category: Artificial Intelligence

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

Montoux’s next-generation decision science platform for insurers uniquely combines actuarial and data science in its software, providing a modern, flexible, and sophisticated alternative to legacy actuarial software. Learn how the AI-driven model replication module is specifically developed to support an insurer’s transition to cloud and reduces the time, cost, and effort required to migrate to a new platform.

As multi-tenant SaaS providers look to leverage machine learning services, they must consider how they’ll protect the data that flows in and out of these services from different tenants. Learn how tenant isolation of machine learning services can be achieved using AWS IAM, and how the integration between IAM, Amazon SageMaker, and many other AWS services provide developers with a rich set of mechanisms that can be applied to realize tenant isolation goals.

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

Some contact center platforms don’t offer native integrations with AWS AI/ML services. Consequently, customers must learn to live with a “just enough” contact center until their license agreements expire, look for other options beyond AWS, or wait for the platform provider to build the integrations in future releases. Learn how Universal Speech Solutions (also known as Unispeech) created AWS AI/ML plugins that enable the non-native integrations.

Using state-of-the-art technology, user-defined events, and identification of operational exceptions, TaskWatch is redefining the way Cincinnati/Northern Kentucky International Airport (CVG) gains efficiencies through automation. Adding TaskWatch’s computer vision platform that works in conjunction with AWS Panorama, the Airport sees, adds, and explores distinct use cases for advanced AI. TaskWatch is leading the way for continuous improvement in capacity management and productivity.

Today’s global marketplace relies heavily on contact centers for streamlining, maintaining, and maximizing customer service and sales at scale. Explore the role of machine learning solutions in transforming contact centers and the key aspects of Quantiphi’s contact center intelligence (CCI) solution built on AWS. Learn how it helped a U.S.-based consumer healthcare organization address contact center challenges by using custom artificial intelligence and ML techniques.

Leveraging data to make better decisions is critical for driving optimal business outcomes. Palantir empowers organizations to rapidly extract maximum value from one of their most valuable assets—their data. Palantir Foundry solves for the real-world application of AI, and not how it works in the lab. Effective AI is impossible without a trustworthy data foundation, a representation of an institution’s decisions, and the infrastructure to learn from every decision made.

XAPP AI is an AWS Machine Learning Competency Partner that provides conversational AI solutions empowering the next generation of customer service and rapid ROI. Learn about a conversational self-service (CSS) solution created by XAPP AI and powered by AWS AI services. The solution gives you the ability to provide this level of self-service experience by allowing users to ask natural language questions and get accurate answers from existing corporate knowledge bases.

With exponential increases in the amount of visual data such as images and videos being generated, image analytics is fast becoming a major business driver for many organizations. The Deloitte Image Recognition Framework is a cloud-based image recognition platform leveraging machine learning to automatically distinguish between two images. It helps customers deploy image recognition and analytics solutions and customize it per their business requirements.