Retaining customers purely depends on the relationship a retailer has with its visitors. Providing appealing offers and personalized discounts may be one of the parameters in strengthening the relationship. However, the majority of customer relationships happen with after-sales services. How well a retailer provides maintenance of the product? And, How quickly the customer receives support from the retailer? Nevertheless, the major challenge associated with customer relationships is the retailers' lack of maintenance and support.

Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.

We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as operation research or urban science, and more generally, it is crucial for businesses' planning and decision making. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location provided their features and the potential customers' characteristics in a given region. BSIM explicitly accounts for the competition among the competitive facilities through a probability value determined by evaluating a store-specific Gaussian distribution at a given customer location. We propose a scalable variational inference framework that, while being significantly faster than competing Markov Chain Monte Carlo inference schemes, exhibits comparable performances in terms of parameters identification and uncertainty quantification. We demonstrate the benefits of BSIM in various synthetic settings characterised by an increasing number of stores and customers. Finally, we construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions. We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.

When you deploy intelligent search in your organization, two important factors to consider are access to the latest and most comprehensive information, and a contextual discovery mechanism. Many companies are still struggling to make their internal documents searchable in a way that allows employees to get relevant information knowledge in a scalable, cost-effective manner. A 2018 International Data Corporation (IDC) study found that data professionals are losing 50% of their time every week--30% searching for, governing, and preparing data, plus 20% duplicating work. Amazon Kendra is purpose-built for addressing these challenges. Amazon Kendra is an intelligent search service that uses deep learning and reading comprehension to deliver more accurate search results.

In 2017, we launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Today, I'm very happy to announce the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call. Each discussion with potential or existing customers is an opportunity to learn about their needs and expectations. For example, it's important for customer service teams to figure out the main reasons why customers are calling them, and measure customer satisfaction during these calls. Likewise, salespeople try to gauge customer interest, and their reaction to a particular sales pitch.

Cognitive retail is the future that's driving retail stores both online and offline establishments. Despite the steep learning curve involved by all the stakeholders in terms of implementations, it's the prospect of diverse benefits that is luring retail stores towards cognitive computing. In this new retail world, the cognitive consumer expects great services at all times. Brands must deliver on such expectations. Cognitive retail affects almost every aspect of a retail business from marketing to supply chain to IT, e-commerce and merchandising.

If you're wondering which company makes the best streaming players for the least amount of money, you might not expect the answer to be Walmart. Walmart's $25 Onn FHD Streaming Stick and $30 UHD Streaming Device both undercut the cheapest comparable Roku and Fire TV streamers, yet the hardware doesn't seem compromised despite the low price. Meanwhile, Google's Android TV software provides a slick streaming menu, powerful voice search, and the ability to cast video from your phone. They don't support Dolby Vision, Dolby Atmos, or HDR10, and I had trouble getting TV volume and power controls to work on the cheaper FHD Streaming Stick. But if that doesn't happen to you, and your streaming needs aren't overly demanding, Walmart's devices are surprisingly hard to beat.

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 extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage of custom callback steps. This feature lets you include tasks that are performed using other AWS services, third parties, or tasks run outside AWS. Before the launch of this feature, steps within a pipeline were limited to the supported native SageMaker steps.

It's a great feeling to see our retailers reopen nationwide. As customers, we have waited to walk into our favorite stores, eagerly finding new merchandise and admiring new floor layouts. At the same time, retailers are keen to understand how customers interact inside their physical stores. Tech innovations, especially powered by AI, can reveal insights about customers that retailers may not see in plain sight. Today, I'm excited to focus on spatial intelligence -- technology that measures how people and objects move and interact in a given space.

All the sessions from Transform 2021 are available on-demand now. As part of VentureBeat's series of interviews with women and BIPOC leaders in the AI industry, we sat down with Dr. Katia Walsh, chief strategy and artificial intelligence officer, Levi Strauss & Co. In her career she has forged paths for people from every intersection of race, culture, class, and education, giving them the tools they need in an AI- and data-centric world to be creative, solve problems, develop new solutions, and change the game in their roles across their companies. VB: Could you tell us about your background, and your current role at your company? I started my career as a journalist in communist Bulgaria, where I personally experienced the power of information through a story I wrote while still in high school.