Demand forecasting is a central component for many aspects of supply chain operations, as it provides crucial input for subsequent decision making like ordering processes. While machine learning methods can significantly improve prediction accuracy over traditional time series forecasting, the calculated predictions are often just point estimations for the conditional mean of the underlying probability distribution, and the most powerful approaches, like deep learning, are usually opaque in terms of how its individual predictions can be interpreted. Using the novel supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted instead of single numbers. While metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare and this work proposes new techniques for both qualitative and quantitative evaluation methods. Additionally, each single prediction obtained with this framework is explainable. This is a major benefit in particular for practitioners, as this allows them to avoid "black-box" models and understand the contributing factors for each individual prediction.

Walmart has had drone delivery ambitions for years now, and today they've announced a partnership with Zipline for on-demand delivery of "health and wellness" products. Zipline drones aren't the quadcopters that most think of for these types of delivery services. Instead, they're gliders that have longer range and won't just drop out of the sky if something fails. Trial deliveries using Zipline's drones will take place near Walmart headquarters in northwest Arkansas with a plan to start early next year. Walmart says that the Zipline drones will be able to operate within a 50-mile radius, and they produce no carbon emissions.

Apple will stage its traditional post-Labor Day product reveal on Tuesday, where it is expected to tout new editions of the Apple Watch and iPad. Along the way, there will be new things for Siri to do as well on the iPad, as part of the iOS mobile operating system upgrade. Siri is the oft-maligned but heavily used personal assistant. This year, Siri will tout a "completely new look," with "over 20x more facts than just three years ago." Yes, Apple actually says this, on the promo page for the iOS 14 upgrade, which has traditionally been made available in September.

Amazon SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design allows you to pick and choose the features that suit your use cases at different stages of the ML lifecycle. Amazon SageMaker offers capabilities that abstract the heavy lifting of infrastructure management and provides the agility and scalability you desire for large-scale ML activities with different features and a pay-as-you-use pricing model. In this post, we outline the pricing model for Amazon SageMaker and offer some best practices on how you can optimize your cost of using Amazon SageMaker resources to effectively and efficiently build, train, and deploy your ML models. In addition, the post offers programmatic approaches for automatically stopping or detecting idle resources that are incurring costs, allowing you to avoid unnecessary charges. Machine Learning is an iterative process with different computational needs for prototyping the code and exploring the dataset, processing, training, and hosting the model for real-time and offline predictions. In a traditional paradigm, estimating the right amount of computational resources to support different workloads is difficult, and often leads to over-provisioning resources. The modular design of Amazon SageMaker offers flexibility to optimize the scalability, performance, and costs for your ML workloads depending on each stage of the ML lifecycle. The following diagram is a simplified illustration of the modular design for each stage of the ML lifecycle.

You must specify the region where your S3 bucket is located. You can find the right region in the list of buckets on the Amazon S3 console. The user you use must have read access to the specified S3 bucket. For more information about securely granting access to S3 buckets to a specific user, see Writing IAM Policies: How to Grant Access to an Amazon S3 Bucket. You should see something similar to the following screenshot. If you prefer to have an instance of TensorBoard permanently running and accessible to your whole team, you can deploy it as an independent application in the cloud. One of the easiest ways to do this without managing servers is AWS Fargate, a serverless compute engine for containers. The following diagram illustrates this architecture.

The retail industry has been going through digital transformation for a long time now. Introduction of digitization has increased the speed and accuracy of branches all over the retail business. With detailed data at hand, industries have the opportunity to make data-driven decisions. In this article, we take a look at how AI is changing the retail industry? Before we understand the why of AI in the retail industry, let's take a look at how AI is reshaping the retail business.

Kroger Co. has spent years--and hundreds of millions of dollars--investing in technology to give it a digital edge in the grocery business. But when the coronavirus changed customers' buying habits overnight, the grocery chain wasn't as ready for the online shift as some of its competitors. The nation's biggest grocer, Kroger has poured money into projects ranging from a self-driving grocery delivery robot to a partnership to sell goods in China through Alibaba Group Holding Ltd. It also bet that a delivery model using remote fulfillment centers, popular in Europe, would resonate stateside. Yet, when the pandemic sent a tsunami of customers ordering groceries online for the first time, it was unable to meet higher demand. The wide-ranging investments slowed adoption of technology for grocery delivery, leaving Kroger behind some of its competitors, said former executives, current employees and a vendor.

Mobile Development has become one of the most critical aspects of many companies. This is due to the acquisition of a more organic base of clients. Without a mobile-optimized solution, a company could face the question of lagging behind its competitors. Given that revenue from mobile apps' development has reached a new peak, people are following the trend in app development. Both the users and the developers follow the path of making life more comfortable.

In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platforms also use the same data corresponding to customers' actions to learn how these products must be ranked or ordered. These interactions in the underlying learning process, however, may incentivize sellers to artificially inflate their position by employing fake users, as exemplified by the emergence of click farms. Motivated by such fraudulent behavior, we study the ranking problem of a platform that faces a mixture of real and fake users who are indistinguishable from one another. We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users. To overcome this deficiency, we develop efficient learning algorithms under two informational environments: in the first setting, the platform is aware of the number of fake users, and in the second setting, it is agnostic to the number of fake users. For both these environments, we prove that our algorithms converge to the optimal ranking, while being robust to the aforementioned fraudulent behavior; we also present worst-case performance guarantees for our methods, and show that they significantly outperform existing algorithms. At a high level, our work employs several novel approaches to guarantee robustness such as: (i) constructing product-ordering graphs that encode the pairwise relationships between products inferred from the customers' actions; and (ii) implementing multiple levels of learning with a judicious amount of bi-directional cross-learning between levels.

Your future Walmart order might be delivered via drone. The retail giant announced the launch of an on-demand drone delivery pilot program in Fayetteville, North Carolina Wednesday with Flytrex, an end-to-end drone delivery company. In a blog post, Tom Ward, Walmart senior vice president of customer products, said the pilot focuses on delivering select grocery and household essential items from Walmart stores using Flytrex's automated drones. "The drones, which are controlled over the cloud using a smart and easy control dashboard, will help us gain valuable insight into the customer and associate experience – from picking and packing to takeoff and delivery," Ward said. Save better, spend better: Money tips and advice delivered right to your inbox.