Category: Artificial Intelligence

AWS artificial intelligence services can serve as a kind of ready-made building block that enable companies of all sizes and sectors to gain experience and create their own AI services, without having to build the fundamental functions from the bottom up. Via the cloud, companies can access AWS AI services and create their own chatbots, image analysis, or personalization tools, for example. To implement and customize these services, companies can draw on the expertise of Trivadis – Part of Accenture.

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and Amazon SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.

A closed loop assurance system predicts network events, such as faults and congestions, that are highly probable of causing service degradation or interruption, and automatically take preventive actions to avert service disruptions. Learn how Infosys leveraged AWS data streaming, data analytics, and machine learning services to ingest, process, and analyze high volumes of data from disparate sources; and to build ML models to predict network events that cause service degradation.

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.