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.

Too many items and too few items are both scenarios that are bad for business. Massive incremental profit can be unlocked by retailers managing orders and inventory effectively. But as this requires the processing of data for a huge number of stock keeping units (SKUs), which often include perishable goods and items that are ordered daily, it is also a significant challenge. Retailers used to rely solely on the data from previous years to predict future sales (and therefore manage their inventory), but this method is only useful up to a point. However, machine learning has now evolved to the stage that it can provide accurate predictive models using different signals based on how they influence purchases.

As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount of experimentation required before finalizing a version of a model. Amazon SageMaker, a fully managed ML service, enables organizations to put ML ideas into production faster and improve data scientist productivity by up to 10 times. Your team can quickly and easily train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production-ready environments.

This is a guest post by Mick McCluskey, the VP of Product Management at Enghouse EspialTV. Enghouse provides software solutions that power digital transformation for communications service operators. EspialTV is an Enghouse SaaS solution that transforms the delivery of TV services for these operators across Set Top Boxes (STBs), media players, and mobile devices. A large audience of consumers use TV services, and several of these groups may have disabilities that make it more difficult for them to access these services. To ensure that TV services are accessible to the broadest possible audience, we need to consider accessibility as a key element of the user experience (UX) for the service.

Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average time it takes to render a page for an end user) or application availability (the duration of time the application was operational). One challenge that most organizations face today is detecting anomalies in operational metrics, which are key in ensuring continuity of IT system operations. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined.

What springs to mind when you think of advertising? Or perhaps trendy people swapping catch phrases in a converted warehouse?' Well, more of the creative work these days is not being done by humans at all. When Dixons Carphone wanted to push shoppers towards its Black Friday sale, the company turned to Artificial Intelligence (AI) software and got the winning line "The time is now". Saul Lopes, head of customer marketing at Dixons Carphone, thinks it worked because it didn't have the words Black Friday in it.

My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. Every 1 – pager has a simple picture, making it easy to remember.

Mr Lopes knows that in an age of information overload consumers are becoming harder to reach as online sales patter diminishes their appetite for any message. As Phrasee comes armed with a terrific linguistic arsenal and is divorced from the individual points of view that shape the words of human copywriters he thinks it suits our jaded eyes.