Tag: Artificial Intelligence

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.

By powering personalized, relevant, and timely digital interactions built on guest behavior data, Braze can help AWS lodging customers generate additional revenue and engage guests for the long run using personalization. Learn how a hotel customer can rapidly and simply transform their web booking engine into a digital user engagement solution offering a personalized booking experience including upsell capabilities.

To quickly build intelligent data-driven workflows, organizations need business analysts to work with data scientists and development teams to unlock useful insights from unstructured or semi-structured data. Learn how KNIME’s end-to-end data science product portfolio helps bridge the gap between the ideation and productionalization steps of data science projects, while also assisting in the communication of key data science aspects between teams.

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.

Insurance companies need a solution to effectively manage vehicle insurances provided to their end customers, and telematics solutions are used to monitor and manage these fleets. Learn how IBM developed Telematics Hub that focuses on risk analysis, crash and claim management, and more. The solution can be extended to additional use cases and integrate with many third-party IoT devices and applications for vehicle data collection.

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.

From a wide range of AWS Partner success stories to new partner programs launched at AWS re:Invent, here’s a look at the most popular APN Blog posts we shared in 2021. Our goal with this blog has always been to share timely and relevant news, technical solutions, partner stories, and more from Amazon Web Services and the AWS Partner Network (APN) specifically. Thank you for reading the APN Blog, and Happy New Year to all our AWS Partners and customers!

Customer experience is at its best when a customer perceives the experience offered is unique and aligns to their preferences. The need to engage, at a very personal level, becomes key. Learn how Capgemini’s data and analytics practice implements customer intelligence platforms on AWS to help companies build a unified data hub. This enables customer data to be converted into insights that can be used for reporting and building AI/ML predictive analytics capabilities.

Kubernetes is hardware-agnostic and can work across a wide range of infrastructure platforms, and Kubeflow—the self-described machine learning toolkit for Kubernetes—provides a Kubernetes-native platform for developing and deploying ML systems. Learn how D2iQ Kaptain on AWS directly addresses the challenges of moving ML workloads into production, the steep learning curve for Kubernetes, and the particular difficulties Kubeflow can introduce.