Unleash the power of machine learning (ML) through hands-on learning and compete for prizes and glory. The AWS DeepRacer League is the world's first global autonomous racing competition driven by reinforcement learning; bringing together students, professionals, and enthusiasts from almost every continent. I'm Tomasz Ptak, a senior software engineer at Duco, an AWS Machine Learning Hero, an AWS DeepRacer competitor (named Breadcentric), a hobbyist baker, and a leader of the AWS Machine Learning Community on Slack, where we learn, race, and help each other start and grow our adventures in the cloud. It's my pleasure to unveil the exciting details of the upcoming 2022 AWS DeepRacer League season. It's a complete program that has helped over 175,000 individuals from over 700 businesses, educational institutions, and organizations begin their educational journey into machine learning through fun and rivalry.

Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available. Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale.

Accelerating their net zero ambitions has been top of retailer's agendas for a number of years, with the sector considering itself one of the most ambitious when it comes to tackling climate change, but the real picture is alarming. According to research from Microsoft, only 28 per cent of UK retail organizations are currently on course to be net zero by 2050 – in stark contrast to the national average of 41 per cent. Furthermore, as 74 per cent of organizations within all sectors describe a'one foot in, one foot out' approach on sustainability, whereby strong ambitions have yet to be translated into meaningful action, retailers can't afford to be left behind. Harnessing available technology, such as data analytics and AI, to accurately record their progress is one of the fundamental steps to securing their continued success, in an ever more competitive marketplace. Contributing to the protection of the future of the planet is obviously the core reason for improving the sustainability performance of any business, but aside from that, retailers should view boosting their sustainability credentials as a way of remaining competitive and relevant in a fast-changing world.

One of the biggest challenges for an ecommerce store is shopping cart abandonment. As a store owner, you have done all the work to get the customer interested, finally engaged with them for them to come to your web site or the app and only to lose them when they were ready to make a purchase. That is one of the biggest nemesis for the e-commerce store owner. The average cart abandonment rate across all the industries is north of 70%. That is even worse for Mobile users which is 85% (15% higher).

Operations in the telecommunications industry is often said to be one of the most complex aspects of the business to run, and the most successful telcos tend to be those that outperform at this task. It requires a simultaneous, coordinated, and dynamic approach across business units, each of which alone would be a giant business to run. In recent years, artificial intelligence has had the potential to simplify the task by optimizing various functions that make up operations. Telcos are only just beginning to utilize that promise, with operators finding success with AI solutions that help optimize service operations journeys, such as the in-store customer experience, call center use, and deployment of employees in stores, call centers, and the field. The intensely challenging economic landscape that telcos have had to navigate in recent years makes the prospect of investment in new solutions daunting.

Customer experience or CX focuses on the relationship between a brand and its customers. It includes every bit of interaction, brief or long, even if the conversation doesn't result in a purchase. Having said that, a few years ago, customer experience meant how attentive retailers were to address customers and their needs in a store. And if this was executed well, the results were: increase in sales, customer acquisition, and retention, thereby strengthening customers' trust in the brand. Today, how customers and brands interact with each other has evolved.

At the first glance, fashion – that is typically pushed by art, instinct, and creativity- looks as though it would not be a likely match for a clinical, analytical, and logic-driven technology like artificial intelligence. However, AI technology holds extremely good scopes for the fashion industry because of the massive facts set around ancient fashion trends, income patterns, marketplace swings, and customer preferences. For instance, did the global recession in 2009 impact people's style of experience and buying behavior? These are evaluations that might yield interesting effects and even higher yet, expect destiny behavior and income in reaction to worldwide events. When it involves AI in the fashion industry, the technology is enhancing nearly every element of the industry's functioning, in particular e-commerce.

Predictive maintenance can be an effective way to prevent industrial machinery failures and expensive downtime by proactively monitoring the condition of your equipment, so you can be alerted to any anomalies before equipment failures occur. Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are the foundational elements for enabling predictive maintenance solutions. However, even after installing the ad hoc infrastructure, many companies use basic data analytics and simple modeling approaches that are often ineffective at detecting issues early enough to avoid downtime. Also, implementing a machine learning (ML) solution for your equipment can be difficult and time-consuming. With Amazon Lookout for Equipment, you can automatically analyze sensor data for your industrial equipment to detect abnormal machine behavior--with no ML experience required.

Amazon SageMaker is a managed service that makes it easy to build, train, and deploy machine learning (ML) models. Data scientists use SageMaker training jobs to easily train ML models; you don’t have to worry about managing compute resources, and you pay only for the actual training time. Data ingestion is an integral part of […]

That retail has experienced extreme disruption in recent years is beyond questioning. Even before Covid turned the world on its head, headlines about the so-called "retail apocalypse" were near-ubiquitous in the media. Since then, we've seen lockdowns, fluctuating openings and closings, some firms going out of business altogether, celebrations of essential retail workers and a surge in online shopping that brought record profits while yielding more ambiguous results for others. And now, with ongoing supply chain disruption, inflation and a tight labor market, it's clear that the retail sector still faces substantial challenges. But these challenges also represent opportunity, and harnessing the power of digital transformation will remain central to every business leader serious about thriving in the post-Covid world.