Category: Artificial Intelligence

Learn now Provectus looked into how machine learning models were prototyped and evaluated at VTS, and then delivered a template-based solution enabling their data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. The resulting coherent set of templates, with usage cookbook and extension guidelines, was applied successfully on an ML model that predicted leasing outcomes.

Healthcare customers use Snowflake to store all types of clinical data in a single source of truth. One method for gaining insights from this data is to use Amazon Comprehend Medical, which is a HIPAA-eligible natural language processing service that uses machine learning to extract health data from medical text. Learn how the Snowflake Data Cloud allows healthcare and life sciences organizations to centralize data in a single and secure location.

Organizations hosting customer-specific machine learning models on AWS have unique isolation and performance requirements and require a solution that provides a scalable, high-performance, and feature-rich ML platform. Learn how Amazon SageMaker Pipelines helps you to pre-process data, build, train, tune, and register ML models in SaaS applications. We’ll focus on best practices for building tenant-specific ML models with particular focus on tenant isolation and cost attribution.

Healthcare customers are adopting fast healthcare interoperability resources (FHIR) as a way to exchange healthcare information in a secure and compliant manner. Aligning on a common data model streamlines healthcare application development and the adoption of machine learning. Learn how to visualize and navigate FHIR data on AWS by using eSante Clarity, Cognosante’s FHIR viewer. Clarity can access FHIR data on AWS and navigate the clinical dataset within.

As a modernization service provider, Effectual is focused on a cloud-first approach that includes end-to-end managed and professional services for every stage of IT modernization. Effectual views managed services from a holistic perspective that prioritizes security and compliance. Learn how they were able to integrate AWS next-generation monitoring, as well as operational services like Amazon DevOps Guru, to enhance the company’s managed services offering.

Recognizing a user’s signature is an essential step in banking and legal transactions, and typically involves relying on human verification. Learn how Capgemini uses machine learning from AWS to build ML-models to verify signatures from different user channels including web and mobile apps. This ensures organizations can meet the required standards, recognize user identity, and assess if further verifications are needed.

The continuous interaction between humans and animals in slaughterhouses can lead to animal welfare deviations which can occur in different forms. Learn how Deloitte’s AI4Animals solution is capable of detecting these welfare deviations in order to improve the conditions of animals in slaughterhouses. This is accomplished by using AWS Panorama, a machine learning appliance and software developer kit (SDK) that allows organizations to bring computer vision to their on-premises cameras.

Increasing penetration of analytics in the life sciences industry is expected to drive significant growth for businesses in the coming years. Learn about Accenture’s life sciences data and analytics accelerator which enables customers to respond to these challenges and use data for their competitive advantage. Particular focus is given to the commercial domain and use of analytics to increase customer engagement and optimize sales and marketing.

Customer experience is at its best when a customer perceives the experience offered is unique and aligns to their preferences. The need to engage, at a very personal level, becomes key. Learn how Capgemini’s data and analytics practice implements customer intelligence platforms on AWS to help companies build a unified data hub. This enables customer data to be converted into insights that can be used for reporting and building AI/ML predictive analytics capabilities.

Kubernetes is hardware-agnostic and can work across a wide range of infrastructure platforms, and Kubeflow—the self-described machine learning toolkit for Kubernetes—provides a Kubernetes-native platform for developing and deploying ML systems. Learn how D2iQ Kaptain on AWS directly addresses the challenges of moving ML workloads into production, the steep learning curve for Kubernetes, and the particular difficulties Kubeflow can introduce.