Tag: Amazon SageMaker

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.

The Digital Platform for Next Generation Agriculture is built by Tata Consultancy Services (TCS) to address challenges faced by farmers and associated enterprises including agri-input companies. It enables the use of predictive analytics for crop acreage, crop yield, crop health, soil moisture status, weather and pest forecasts, and resource quality assessment to help farmers minimize risk. The platform comprises of big-geospatial, IoT, and climate data analysis framework built on AWS.

Infosys Digital Health Platform is a connected health platform powered by AWS and is designed to help patients lead a healthy life. It achieves this through regular and in-depth insights on a patient’s daily habits and streaming of vital health parameters from different connected devices. It also tracks symptoms and quality-of-life indicators. With this information, the platform analyses data for multiple therapeutic areas and provides necessary insights.

Like many of today’s leading media and streaming platforms, PBS wanted to take its overall user experience to the next level. That’s why PBS approached AWS Premier Tier Consulting Partner ClearScale, a leader in machine learning. ClearScale came up with a detailed roadmap for tackling PBS’s recommendation system project that included data operations, MLOps, and demonstrational user interface. Together, PBS and ClearScale decided to move forward with an AWS-powered solution on top of Amazon Personalize.