Walmart said Tuesday it will start farming out its delivery service, using contract workers, autonomous vehicles and other means to transport rival retailers' products directly to their customers' homes as fast as just a few hours. The nation's largest retailer said it will dispatch contract workers from its Spark delivery network, which was launched in 2018, to merchants' stores to pick up items and then bring them to shoppers. Over the past year, Walmart has doubled Spark's coverage to more than 500 cities nationwide, providing access to more than 20 million households. Walmart, which is based in Bentonville, Ark., aims to tap into its ties with local communities, particularly businesses in rural areas that have struggled to provide their own delivery services. The strategy will pit Walmart against delivery services run by the likes of Uber and DoorDash. And it comes as Walmart moves to expand its sources of profits and revenues beyond its core retail businesses.

Walmart has announced a delivery service for local businesses, which should be up and running by the end of the year. It plans to use drones and self-driving cars as part of the Walmart GoLocal infrastructure. Earlier this year, Walmart invested in Cruise after previously running a delivery pilot with GM's autonomous vehicle startup. Local retailers might be able to keep using their current commerce platform and hook it into GoLocal. It's a white-label service, so deliveries won't be made by Walmart-branded vehicles.

Seven-Eleven Japan Co. plans to expand its home delivery service by making it available through its convenience stores across the country totaling some 20,000 by around 2025, according to informed sources. Through the expansion of the service offered via a dedicated website, Seven & I Holdings Co., the parent of Seven-Eleven Japan, aims to capitalize on growing demand for online shopping amid the coronavirus pandemic, at a time when the convenience store market is believed to be in a state of saturation. Currently, about 550 Seven-Eleven convenience stores in Tokyo, Hiroshima Prefecture and Hokkaido are offering the service to deliver goods from their store shelves to customers' homes, workplaces and elsewhere within as fast as 30 minutes from the time of order. Customers within a 500-meter radius of a Seven-Eleven outlet can select what to buy from some 3,000 items sold at the store and have them delivered. They need to place at least ¥1,000 in orders excluding tax each time and pay a delivery fee of ¥330 including tax.

Seven-Eleven Japan Co. plans to expand its home delivery service by making it available through its convenience stores across the country totaling some 20,000 by around 2025, according to informed sources. Through the expansion of the service offered via a dedicated website, Seven & I Holdings Co., the parent of Seven-Eleven Japan, aims to capitalize on growing demand for online shopping amid the coronavirus pandemic, at a time when the convenience store market is believed to be in a state of saturation. Currently, about 550 Seven-Eleven convenience stores in Tokyo, Hiroshima Prefecture and Hokkaido are offering the service to deliver goods from their store shelves to customers' homes, workplaces and elsewhere within as fast as 30 minutes from the time of order. Customers within a 500-meter radius of a Seven-Eleven outlet can select what to buy from some 3,000 items sold at the store and have them delivered. They need to place at least ¥1,000 in orders excluding tax each time and pay a delivery fee of ¥330 including tax.

In one paragraph and in Figure P-1, let me show you my goal for the book. I introduce a number of data structures that explain how to organize information using primitive fixed-size types, such as 32-bit integer values or 64-bit floating point values. More complicated algorithms, especially graph algorithms, rely on a number of fundamental abstract data types, which I introduce as needed, such as stacks or priority queues. These data types provide fundamental operations that can be efficiently implemented by choosing the right data structure. By the end of this book, you will understand how the various algorithms achieve their performance.

From automating the creation of personalized videos to enabling new virtual customer experiences, these companies are deploying AI to help brands and retailers create engaging digital content. Brands and retailers are relying more and more on digital content -- which can range from product images for e-commerce sites to virtual try-on features to online videos -- to increase brand awareness, convert online shoppers, and boost loyalty. Video content is gaining particular momentum, with the number of times execs have mentioned the term during earnings calls shooting up in Q2'21. But given these ever-growing digital content needs, a trend accelerated by the Covid-19 pandemic, conventional production approaches may not be sufficient for brands and retailers to deliver personalized and engaging content at scale. Enter synthetic media -- images, videos, sounds, or any other form of content that has been generated, edited, or enabled by artificial intelligence.

From shopper emotion detection to in-store robotic warehouses, we highlight the technologies and trends bringing retail stores into the future. The year is 2030, and you're going to the store. Before you enter, you receive a text message from a store employee: the shirt you reserved is waiting for you. Once you get inside, maybe you spot a robot ferrying around candy bars, or you walk under a flying in-store drone. Or maybe what stands out to you is what you don't see: cashiers and checkout lines.

I have been at Lucidworks for about eight years. My background is in software engineering with an emphasis on analytics and distributed computing. I was brought in to Lucidworks by the board to lead the company through a transition from an open source support and services company to delivering proprietary search and AI solutions. We have a team of around 250 employees that are distributed across the country and the world. Back in 2005, pre-Lucidworks, I was a part of the founding team at Splunk.

I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models. The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable "do-it-yourself" ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the "do-it-yourself" descriptions provided in the book. In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably.

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise machine learning (ML) environments. In this second and final part, we provide instructions for deploying the solution from the source code GitHub repository to your account or accounts and experimenting with the delivered SageMaker notebooks. The provided CloudFormation templates provision all the necessary infrastructure and security controls in your account. An Amazon SageMaker Studio domain is also created by the CloudFormation deployment process. The following diagram shows the resources and components that are created in your account.