Millennials and Gen Z buyers – the dominant market segment – are increasingly voicing a preference for sustainably produced, longer-lasting clothing. Second-hand and rental fashion retail is also gaining popularity as buyers recognize the negative impact of over-shopping and then discarding. In 2019 alone, 52% of millennials in Britain bought secondhand clothes – a significant number. And brands are responding to this shift by pivoting their practices with the help of data insights from AI solutions.

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with existing source control systems such as GitHub, GitHub Enterprise, and Bitbucket. This new capability also allows you to utilize existing installations of Jenkins for orchestrating your ML pipelines.

Computer monitors that support HDMI 2.1, the latest HDMI standard, are beginning to trickle into online retailers. They sell at extremely high prices (when they're available at all). Even the most affordable HDMI 2.1 monitors, like the Gigabyte Aorus FI32U and Acer Nitro XV282K KV, are priced near $1,000. The high price of HDMI 2.1 implies it's important, but the truth is more nuanced. HDMI 2.1 brings new features to the table, but they're relevant only to people with specific needs.

Reducing the number of failures in a production system is one of the most challenging problems in technology driven industries, such as, the online retail industry. To address this challenge, change management has emerged as a promising sub-field in operations that manages and reviews the changes to be deployed in production in a systematic manner. However, it is practically impossible to manually review a large number of changes on a daily basis and assess the risk associated with them. This warrants the development of an automated system to assess the risk associated with a large number of changes. There are a few commercial solutions available to address this problem but those solutions lack the ability to incorporate domain knowledge and continuous feedback from domain experts into the risk assessment process. As part of this work, we aim to bridge the gap between model-driven risk assessment of change requests and the assessment of domain experts by building a continuous feedback loop into the risk assessment process. Here we present our work to build an end-to-end machine learning system along with the discussion of some of practical challenges we faced related to extreme skewness in class distribution, concept drift, estimation of the uncertainty associated with the model's prediction and the overall scalability of the system.

As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for some use-cases. With other critical use-cases, such as fraud detection for financial transactions, product quality in manufacturing, or analyzing video surveillance in real-time, customers are faced with the challenges that come with having to move that data to the cloud first. One of the challenges customers are facing with performing inference in the cloud is the lack of real-time inference and/or security requirements preventing user data to be sent or stored in the cloud. Tens of thousands of customers use Amazon SageMaker to accelerate their Machine Learning (ML) journey by helping data scientists and developers to prepare, build, train, and deploy machine learning models quickly.

Reviewers played an important role in helping people make purchases in the past, and still play an important role in today's world. There is, however, a growing skeptical element in the population. In the wake of last year's controversies over fake content, customers have changed the way they look at the information they find online, even if it looks like it's. It is inconceivable that the media have ever been as aggressive in their pursuit of truth as they are. A large volume of user generated content can now be analyzed by artificial intelligence. An analytical algorithm was used to analyze 25,000 reviews of hotels that were found across the web and analyzed with machine learning.

We all could use a little help keeping our homes clean and a robot vacuum can do just that. Some robots, like iRobot's Roomba i7 and S9, go one step further by automatically emptying their bins into their clean bases after each job -- so you rarely have to take out its trash. These gadgets come with high price tags, but you can grab either of them for less right now at Wellbots. The online retailer has the Roomba i7 for $699, or $100 off, and the S9 for $949, or $150 off, when you use the codes 100ENGADGET and 150ENGADGET, respectively, at checkout. While not all-time-low prices, they're the best prices we've seen since April.

In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values to vectorized representation, aggregating log data to a daily summary, and more. The quality of your features directly influences your model predictability, and often needs a few iterations until a model reaches an ideal level of accuracy. Data scientists and developers can easily spend 60% of their time designing and creating features, and the challenges go beyond writing and testing your feature engineering code.

You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these devices, deploying and managing ML models at the edge is often difficult. With AWS IoT Greengrass and Amazon SageMaker Edge Manager, you can perform ML inference on locally generated data on Snowball Edge devices using cloud-trained ML models. You not only benefit from the low latency and cost savings of running local inference, but also reduce the time and effort required to get ML models to production. You can do all this while continuously monitoring and improving model quality across your Snowball Edge device fleet.

"That means better performance and faster speeds in our management of inventory, many completion of transactions, submission of invoices to accounts payable and more," he said. Completed in May, the effort across around 9,000 U.S. stores was part of a five-year IT overhaul grouping applications for retail, merchandising, inventory management and finance, among others, on a platform built on S/4HANA, enterprise-resource planning software from Germany-based SAP SE. The SAP system is tied to retail operations, and isn't used for the company's pharmacy operations. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Enterprise resource-planning systems are a mainstay of IT operations at large firms, and house accounting, supply-chain and other core business functions.