Category: Artificial Intelligence

In today’s increasingly digital world, the nature and function of buildings is constantly changing. Facility managers face increasing pressure to adapt to an ever-evolving workplace and regulatory requirements while improving customer comforts and reducing operating costs. Learn about Cognizant’s smart building solution and why this APN Premier Consulting Partner chose AWS for hosting its application, as well as how AWS helps Cognizant fulfill commitments to customers.

Learn how to create a sample custom Box Skill by using HAQM Rekognition Image and AWS Lambda to apply computer vision to image files in Box. This new metadata allows you to quickly find images based on keyword searches, or find images that may be inappropriate and should be moderated. With services like HAQM Transcribe, HAQM Translate, HAQM Comprehend, HAQM Rekognition Video, and HAQM SageMaker, there’s no limit to the ways you can apply AI/ML to your media files.

Many organizations feel that AI will be the biggest disruptor to their industry in the next five years, and many leaders are asking if machine learning is right for their business. We offer an approach to identifying real business value using ML and discuss how to identify and quantify which use cases are the best fit for your industry and how to derive business value with the help of AWS Machine Learning Competency Partners.

Do you want to become more familiar with how your company can use artificial intelligence (AI) and machine learning (ML) but feel a bit lost amongst the buzzwords and hype? Driving business outcomes with AI doesn’t need to be overwhelming. It’s all about exploring which business problems you want to solve, how good predictions can help you achieve those outcomes, and then taking practical steps to get there while implementing an organization-wide AI strategy.

Companies struggling with data science don’t understand the data science life cycle. As a result, they fall into the trap of the model myth. This is the mistake of thinking that because data scientists work in code, the same processes that works for building software will work for building models. Models are different, and the wrong approach leads to trouble. Domino Data Lab shares that organizations excelling at data science are those that understand it as a unique endeavor, requiring a new approach.

Fostering a data-driven culture within your organization isn’t only about technology. It’s also about enabling stakeholders to make better decisions and realizing new opportunities by embracing an AI-driven mentality for solving business problems. In this post, AWS Machine Learning Competency Partner Crayon discusses some of the first steps you should take and the essential questions to ask yourself as you thoughtfully develop your company’s relationship with data.

The nature of data has changed dramatically. Just a decade back, the majority of our data was structured (residing in relational databases) or textual. Now, with the advent of self-driving vehicles, drones, and the Internet of Things (IoT), images and video data are taking the lion’s share of the data storage zoo. As we create more and more data on more and more devices, however, this problem is not going away. In fact, we have reached a point where there aren’t enough people on the planet to label all the data we’re creating.

AWS provides a broad set of managed services for data analytics that, along with a strong APN Partner community, can help you build a scalable, secure, and cost-effective data lake. Customers and APN Partners want to know how to put all these pieces so we created a new poster and video explaining the overall flow of data—from data collection, storage, and processing all the way to analytics and machine learning.

While market adoption of machine learning varies across industry segments, healthcare and life sciences have lots of opportunity to explore. Pharmaceutical and biotech industries, specifically, have low utilization of the large volumes of data they collect and store for regulatory purposes. Aizon is an AWS Partner that offers a number of ML solutions to enhance utilization and management of such data in a way that adheres to Good Manufacturing Practice (GMP) in the cloud.

H2O is an open source data machine learning platform that provides a flexible, user-friendly tool to help data scientists and machine learning practitioners. It was created by H2O.ai, an APN Advanced Partner with the AWS Machine Learning Competency. In this post, we look at setting up an H2O cluster, import data from HAQM S3, create an AWS Lambda deployment package from the model, and finally deploy a RESTful endpoint. Following these steps, you can migrate your H2O Flows to AWS in about 10 minutes.