The capabilities of artificial intelligence (AI) for retailers of all different shapes and sizes has undeniably grown across many sectors in recent years. In today's world, retailers are beginning to develop a legitimate recognition of what it takes to properly appraise, develop and generate AI and ML-enabled solutions of the future, moving past the marketing outbreak that AI once was. Moreover, despite the developments that have been contrived, some retailers have not yet acknowledged the true possibilities of AI and what this entails. It is these retailers that need to question themselves: what do we want to accomplish with AI? What can AI really deliver – and what will this mean for our customers? The opportunities to leverage AI and ML to improve retail operations are exponential for either online, in store or in the warehouse.

Agreed, the headline is slightly presumptuous, in light of the third wave looming large and governments' globally gearing up for the same. That said, with vaccination drives going on in full swing, mask-mandates being lifted, travels resuming, and offices reopening, people are actually heaving a sigh of relief – a virus of good feeling is rippling across the cities. But, please don't take my word for it! America's leading retail brands have gone on to report that their foot traffic has rebounded earlier than expected, so much so that the numbers might exceed their 2019 sales performance. The point is consumer behavior is expected to upend big-time post-pandemic, partly because some customers might want to pursue their pre-pandemic routines, and mostly because some customers might wish to continue with new customer engagement models launched during the pandemic.

Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. How can teams tame complexity and live up to the expectations placed on them? There is no one size fits all when it comes to implementing an MLOps solution on Amazon Web Services (AWS).

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to obtain Amazon SageMaker access policies to provision Studio separately. SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist with the ability to build, train, and deploy ML models quickly. Studio is a web-based integrated development environment (IDE) for ML that lets you build, train, debug, deploy, and monitor your ML models.

Evolving customer expectations and business needs have fueled a digital transformation in China, marked by the integration of cloud computing and artificial intelligence ("AI") into companies' operations. "To survive in this new world, businesses must learn to observe, think, and operate differently," reads Deloitte China's webpage about digital transformation (https://nnw.fm/8rH63). "Digital transformation, the cross-disciplinary power comprising digital, analytics, cloud, cybersecurity, and regulatory compliance, is about embracing digital disruption and unlocking exponential value." Interestingly, Infobird Software (NASDAQ: IFBD), a Software-as-a-Service (SaaS) company offering AI-enabled end-to-end customer engagement solutions in China, has packaged the aspects of digital transformation mentioned above, i.e., cloud computing, analytics, cybersecurity, and digital, into its robust proprietary solutions and is using them to help companies around China adapt to the changing times and transform digitally. IFBD's customer engagement solutions, which integrate the needs of both the customers and the businesses into a single platform, stimulate companies' market performance and growth and enable them to improve their infrastructure.

Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for almost 20 years. Dusty Phillips is a Canadian software developer and an author currently living in New Brunswick. He has been active in the open-source community for 2 decades and has been programming in Python for nearly as long.

TechRepublic's Karen Roby talked with Will Hayes, CEO of Lucidworks, about how artificial intelligence can better help retailers understand customer intent when shopping online. The following is an edited transcript of their conversation. Karen Roby: We all have a tendency, I think, from time-to-time to abandon our carts. We put something in, we take it out, or we just leave it there and we go onto the next site. What typically happens with shoppers, Will?

Amazon's latest set of crowdfunded Echo devices aim for luxury over eccentricity. The retailer has unveiled three new trippy Echo Dot concepts from Belgian fashion designer Diane von Furstenberg (DVF) that you can pre-order today for $60 each. Well, as long as they hit their sales target. Like the trio of weird products Amazon unveiled in February (cuckoo clock anyone?) these dinky speakers are part of the Built It program that borrows from Kickstarter and Indiegogo. Basically, Amazon will only ship out this second round of gadgets if they generate enough consumer interest within 30 days.

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data faces strong overdispersion and sporadic demand, especially zero demand. We discuss resulting modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fails to address the data characteristics due to the considered objective functions. The distributional forecasting provides a suitable modeling approach for to the overcome those problems. The GAMLSS framework allows flexible probabilistic forecasting using low dimensional distributions. We illustrate, how the GAMLSS approach can be applied for the M5 competition data by modeling the location and scale parameter of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawback, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.

Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using SageMaker. These models can be quickly deployed and are pre-trained open-source models from PyTorch Hub and TensorFlow Hub. These models solve common ML tasks such as image classification, object detection, text classification, sentence pair classification, and question answering. The example notebooks show you how to use the 17 SageMaker built-in algorithms and other features of SageMaker.