Tag: Machine Learning

Dynatrace is an AWS migration partner and provides an artificial intelligence-powered platform which delivers full-stack, automated monitoring that goes beyond collecting data. It can help you address challenges in operations, DevOps, cloud migration, and customer experience. In this post, we focus on how Dynatrace shaped their cloud migration and autonomous cloud operations capabilities through their own migration journey from legacy on-premises enterprise application to cloud-native services running on AWS.

We are excited to highlight 29 APN Partners that received new designations in January for our global AWS Competency, AWS Managed Service Provider (MSP), and AWS Service Delivery programs. These designations span workload, solution, and industry, and help AWS customers identify top APN Partners that can deliver on core business objectives. APN Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

For AWS Marketplace and our customers, 2018 was a big year. We had some exciting announcements at AWS re:Invent 2018, where we launched new categories for container products and machine learning. With 30+ new features launched in 2018 for AWS Marketplace, we wanted to highlight the top ones for you, including AWS Marketplace for Machine Learning and Containers, Consulting Partner Private Offers, Enterprise Contract for AWS Marketplace, Flexible Payment Scheduler, and more.

The AWS Competency Program welcomed 39 new APN Partners in November—spanning workload, solution, and industry designations. The AWS Competency Program helps customers identify and choose the world’s top APN Partners that have demonstrated technical proficiency and proven customer success in specialized solution areas. Please join us in welcoming our newest AWS Competency Partners!

Learn how to create a sample custom Box Skill by using Amazon 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 Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Rekognition Video, and Amazon 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.