Tag: Amazon SageMaker

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.

In line with Capgemini Group‘s sustainability vision to become a net zero business by 2040, Sogeti has collaborated with AWS to find a pragmatic solution that is helping to bring this vision to life—the Sogeti Carbon Estimator (SCE). This tool can be used to automatically bring insights into the carbon footprint of any cloud component used to serve technology, including complex AI solutions enabled by MLOps. SCE highlights the goal of many cloud providers and businesses—to unlock the value of cloud sustainably.

As direct to customer (D2C) gains popularity among retailers, there’s an increasing need to mix online and offline experiences to improve customer engagements and sentiment. One such popular channel is popup stores. This post explores a Capgemini solution that uses Amazon Web Services (AWS) to help retailers engage with customers in a smart way. The solution leverages deep learning to enhance the customer experience through gamification and provides key insights and marketing leads to retailers.

A SaaS data platform may run in the account of an ISV or a dedicated account provided by the customer. Learn about the main AWS services SaaS data platforms can integrate with to provide customers with a seamless experience and take advantage of AWS services in order to accelerate their drive to meeting their business goals. Explore how those integrations can be built and examples of AWS ISV Partners who have successfully developed these integrations.

In the healthcare industry, the exchange of data incurs risks as it contains personally identifiable information (PII) and protected health information (PHI). At the same time, not exchanging the data can keep valuable insights hidden. Apexon’s data anonymization and de-identification solution uses sophisticated machine learning algorithms to ensure the exchange of data happens without any risk of PII/PHI being exposed, while allowing organizations to meet compliance and regulatory requirements.

Voice services play a significant role across many industries by enabling real-time and critical communications; but building the computing infrastructure internally to deliver a high-quality voice service involves considerable capital and operational expenses. This post discusses the current challenges in monitoring voice call quality and how Voipfuture’s Qrystal for AWS can be used to bring enriched visibility into your critical communication infrastructure.