Category: Amazon Machine Learning

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

Over two days, Steamhaus conducted an AWS Well-Architected Review on-site with the team who designed, built, and currently manage Elmer at Sperry Rail. Elmer uses machine intelligence to inspect thousands of miles of ultrasound scans collected by Sperry’s inspection vehicles, searching for evidence of cracks in the rail. This partnership allowed quick improvements in efficiency, while ensuring the requirements of running the business day-to-day did not get in the way of improving Elmer.

Many organizations face the challenge of gathering market intelligence on new product and platform announcements made by their partners and competitors—and doing so in a timely fashion. Harnessing these insights quickly can help businesses react to specific industry trends and fuel innovative products and offerings inside their own company.Learn how Accenture helped a customer use AWS to gather critical insights along with key signals and trends from the web using AI and ML techniques.

Amazon re:MARS 2020 is our second annual artificial intelligence event, covering a diverse array of topics and themes related to machine learning, automation, robotics, and space (MARS). The event brings together innovative minds to share new ideas across these rapidly advancing domains. APN Partner are invited to join us as a sponsor at Amazon re:MARS and gain access to decision makers who are starting or accelerating MARS tech initiatives.

Voice data represents a rich and relatively untapped source of information that can help organizations gaining precious insights into their customers and operations. By leveraging a number of AWS services, Deloitte’s speech analytics solution, TrueVoice, can process voice data at scale, apply machine learning models to extract valuable information for this unstructured data, and continuously refine and enrich such models, tailoring them to specific industries and business needs.

Deep learning is inspired by the human brain and once a brain learns to identify an object, its identification becomes second nature. Similarly, as Deep Instinct’s artificial neural network learns to detect more and more types of cyber threats, its prediction capabilities become instinctive. As a result, malware both known and new can be predicted and prevented in zero-time. Deep Instinct’s predictive threat prevention platform can be applied against known or unknown threats, whether it be a file or fileless attack.

With connected IoT solutions built on AWS, businesses can be more proactive with maintenance instead of reactionary, allowing them to fix problems with machinery before they become critical. Reliance Steel & Aluminum Co. teamed up with TensorIoT to solve for this use case. Together, they built an IoT solution on AWS that ensures the maintenance needs of Reliance’s industrial machinery are anticipated and that machines can be serviced before breaking down.

Learn how to seamlessly integrate Amazon Rekognition with the Cloudinary platform, and build an application that automatically tags people in images with names. This solution learns people’s faces from photos uploaded to a “training” folder in Cloudinary. In many cases, a single photo of someone is enough for Amazon Rekognition to learn and then, later on, identify and tag that person. This works in most photograph scenes and even pictures with many other people in them.

Medical imaging is a key part of patient health records and clinical trial workflows. Many facilities still burn medical imaging on CDs, a time-consuming and error-prone process. Ambra Health’s automatic pixel de-identification feature uses Amazon Rekognition and Amazon Comprehend Medical APIs to allow customers to de-identify images and reduce error. Now, it’s easier than ever to deploy an integrated application fabric that elevates healthcare efficiency and care.

Time series forecasting is a common customer need. Amazon Forecast accelerates this and is based on the same technology used at Amazon.com. This new service massively reduces the effort required to automate data updating and model retraining, and it manages this while retaining the granularity of control that data scientists will appreciate and utilize. This post explores the use of this new service for energy consumption forecasting.