Walmart is applying artificial intelligence to the palletizing of products in its regional distribution centers. Since 2017, the discount giant has worked with Symbotic to optimize an automated technology solution to sort, store, retrieve and pack freight onto pallets in its Brooksville, Fla., distribution center. Under Walmart's existing system, product arrives at one of its RDCs and is either cross-docked or warehoused, while being moved or stored manually. When it's time for the product to go to a store, a 53-foot trailer is manually packed for transit. After the truck arrives at a store, associates unload it manually and place the items in the appropriate places.

"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics.

Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their own feature management system. Additionally, you can also have a non-negotiable Java compatibility requirement due to existing data pipelines developed in Java, supporting services that can only be integrated with Java, or in-house applications that only expose Java APIs. Creating and maintaining such a feature management system can be expensive and time-consuming.

You might use traditional methods to forecast future business outcomes, but these traditional methods are often not flexible enough to account for varying factors, such as weather or promotions, outside of the traditional time series data considered. With the advancement of machine learning (ML) and the elasticity that the AWS Cloud brings, you can now enjoy more accurate forecasts that influence business decisions. You will learn how to interpret and format your data according to what Amazon Forecast needs based on your business questions. This post shows you how to prepare your data to optimally use with Amazon Forecast. Amazon Forecast is a fully managed service that allows you to forecast your time series data with high accuracy. It uses ML to analyze complex relationships in historical data and doesn't require any prior ML experience.

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.