Tag: Amazon SageMaker

One of the most exciting evolutions in cloud computing is the proliferation of hybrid cloud and edge computing services. Learn how the Confluent for AWS Wavelength solution was developed to bring Confluent’s data-in-motion technology to the network edge. We’ll demonstrate how the solution extends Confluent for Kubernetes (CFK) to Amazon EKS self-managed nodes in AWS Wavelength Zones to create a geo-distributed Kafka cluster at-scale for industrial IoT and low-latency 5G use cases.

Sales order fulfillment and billing can impact customer satisfaction, and receivables and payments affect working capital and cash liquidity. As a result, the order-to-cash process is the lifeblood of the business and is critical to optimize. Qlik Cloud Data Integration accelerators integrate with Snowflake to automate the ingestion, transformation, and analytics to solve some of the most common SAP business problems, enabling users to derive business insights that can drive decision-making.

A unified, autonomous, and fully digital airline operations solution can help mitigate challenges related to cost inefficiency, latency, or suboptimal decisions and their consequences, including cost, disruption, and customer dissatisfaction. TCS Aviana is an AI-based solution developed by Tata Consultancy Services (TCS) that enables airlines to have comprehensive operations visibility, digital collaboration, and data-driven decision-making through connected insights and intelligence.

Through the Pega Platform and Amazon SageMaker, you can easily streamline the development and operationalization of machine learning models to improve process automation. This allows customers to combine the strengths of cloud, data, and machine learning with AI-powered decisioning and smart workflow capabilities. It also enables customers to operationalize and monetize data and insight, drive process efficiency and effectiveness, and improve customer experience and value.

Many organizations stay on track and achieve objectives by conducting a constructive analysis of strengths and weaknesses, but revenue and profitability analysis are not easy tasks. Explore why Metrodata chose AWS as a trusted platform to build SAP analytics offerings such as revenue and profitability analysis. These offerings empower customers to make business decisions quickly and effectively to expand in new market opportunities and focus on key growth areas.

The AWS Machine Learning Visionaries Partners Report is a quarterly series that tracks, selects, collates, and distributes horizontal technology capabilities enabled by machine learning in areas that AWS expects to be transformative in 1-3 years. The series’ purpose is to share our insights with AWS Partners and to collect their interest, expertise, and insights in co-building along these prioritized themes. The reports include updates on series topics as we see changes in those areas, and new topics will also be added.

Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.

It has become increasingly important for companies to meet security and compliance standards set forth across industries today, but this is particularly a hurdle for smaller ISVs and startups that do not have the resources and budget to navigate the ever-growing list. Learn about an approach and best practices for SOC 2 compliance, and how DuploCloud accelerates time to compliance by natively integrating security controls into mainstream DevOps workflows.

Featurization is one of the most difficult problems in machine learning. Learn how graph features engineered in Neo4j can be used in a supervised learning model trained with Amazon SageMaker. These novel graph features can improve model performance beyond what’s possible with more traditional approaches. Together, these components offer a graph platform that can be used to understand graph data and operationalize graph use cases.

While utilities have historically been rich with data from customers, programs, and assets, many organizations often manage data in siloes. Source data can also be disorganized, with deficiencies in defined quality assurance and quality control processes. Learn how utilities are successfully embracing Accenture’s data-led transformation (DLT) and leveraging accelerators powered by AWS to reach their business objectives and meet regulatory obligations.