Tag: Amazon SageMaker

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.

Data is the most important resource available to modern institutions. The challenge is not necessarily generating, cataloging, or storing data, but operationalizing it. Palantir Foundry is an operations platform that leverages existing data systems and analytics tooling to power smarter decision-making. Learn how Palantir HyperAuto can rapidly integrate data from ERP systems and operationalize that existing data against pressing business problems.

Silicon design companies require huge compute and storage needs to run their electronic design automation (EDA) workloads. Learn about the business and process challenges of silicon design companies and how Nuage, Wipro’s smart orchestrator solution built on AWS, can help accelerate silicon design and time to market. Nuage can do this through prediction and optimization by leveraging the near infinite compute, storage, and other resources available on AWS.

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.

Forward-looking finance teams will build resilience and responsiveness to future disruption. To deliver this strategic mandate, finance offices must be data-driven and share insights that help the organization connect, predict, and adapt within a changing business environment. Learn how Genpact’s data lake analytics reference architecture helps finance teams incorporate functional expertise into AWS solutions with the goal of delivering business impact beyond increased productivity.

A key goal of modern data strategy, whether a data mesh, data fabric, data lake, or data warehouse, is to deliver access to data when and where it’s needed. Learn how AWS and Informatica can combine and automate data governance for access within a data marketplace. This solution combines Information’s data governance architecture and the Informatica Intelligent Data Management Cloud (IDMC) which orchestrates and automates data access management with AWS Lake Formation.

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.