Voice Assistant Technology had a market of its own even before the pandemic hit. Covid-19 and the fear that comes with it have just acted as a catalyst in the adaptation process and radically reshaped consumers' choices. If we go back to 2019, we see that home and voice-connected devices were moving towards becoming consumers' commerce command focus. The trend revolves around millennials who own six devices run on voice assistants except for their phones, and over 31% seem to have made purchases using these devices. With these numbers, we can deduce that the world was already moving towards a more Voice technology and AI-led market before'Global Pandemic' shook our lives.

Artificial Intelligence-powered product recommendations and a more scientific approach to data has seen IKEA lift average order value (AOV) by 2% worldwide. Here Albert Bertlisson, head of engineering at Edge at IKEA Retail (Ingka Group) explains how the company did it. "At IKEA we have multiple places in our customer journey in various channels where different kinds of personalisation can deliver a superior customer experience," he says. "After a while in the broader'recommendations' team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behaviour and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers."

In 1937, two decades after founding his first Piggly Wiggly, supermarket entrepreneur Clarence Saunders opened Keedoozle, a "fully-automated grocery store." Groceries were offered at a steep discount and sample items were displayed in glass cabinets. "To purchase, the customer will insert a key in a hole in the showcase beside the sample article, press a button," TIME Magazine reported at the time. "In the stockroom the proper article will drop on a conveyor belt leading to the cashier's desk. Simultaneously the purchase price is recorded on an adding machine. After all purchases are made, the customer sticks his key into the adding machine, gets his bill. Using another key, the cashier releases the purchases all wrapped for the customer."

Artificial Intelligence (AI) and retail are a good fit. The COVID-19 pandemic has accelerated digital transformation worldwide and is whipping up different business verticals to adopt various AI technologies. As per the UNCTAD survey, more than half of consumers of the emerging and developed economies are shopping online. The part of AI in the retail market in 2020 was valued at USD 1,80 billion and is expected to reach USD 10,90 billion at a CAGR of 35% by 2026. It seems like it is high time for going big or going home for retailers.

The ability to scale machine learning operations (MLOps) at an enterprise is quickly becoming a competitive advantage in the modern economy. When firms started dabbling in ML, only the highest priority use cases were the focus. Businesses are now demanding more from ML practitioners: more intelligent features, delivered faster, and continually maintained over time. An effective MLOps strategy requires a unified platform that can orchestrate and automate complex data processing and ML tasks, and integrates with the latest tooling to best complete those tasks. This post demonstrates the value of using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate an ML pipeline using the popular XGBoost (eXtreme Gradient Boosting) algorithm.

Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.

And walk right out again, without stopping to check out. In just the past three months, California-based AiFi has helped Choice Market increase sales at one of its Denver stores by 20 percent among customers who opted to skip the checkout line. It allowed Żabka, a Polish convenience store chain, to provide faster checkout for morning train commuters. It helped pro-racing team Penske and Verizon run a dinky 200-square-foot store at the Indy500, so race fans could quickly get back to the action. And on Wednesday AiFi announced an expanded partnership with Loop Neighborhood to introduce its computer vision, camera-only platform into stores in California, starting with two Bay Area locations.

The ExpenseIndex field uniquely identifies the expense, and associates the appropriate SummaryFields or LineItemGroups detected to that expense. The most granular level of data in the AnalyzeExpense response consists of Type, ValueDetection, and LabelDetection (optional). Let's call this set of data an AnalyzeExpense element. The preceding example illustrates an AnalyzeExpense element that contains Type, ValueDetection, and LabelDetection. In the preceding example, Amazon Textract detected 16 SummaryField key-value pairs, including VENDOR_NAME: New Store X1 and Order type:Quick Sale. AnalyzeExpense detects this key-value pair and displays it as shown in the preceding example.

As mentioned before, businesses across industries use AI-powered VMS to improve operations. Here are some of the industries that are making the most of their video analytics. Healthcare businesses can use video analytics to get details on whether or not the patients are being entertained with all the needs and requirements they need. In addition, other operations such as patient flow, admission process, guests, etc., can also be monitored to see the improvement opportunities. Retail businesses use video analytics to understand customer behavior and patterns to improve customer experience.

The RealReal's Finance team is looking to hire a Senior Engineer, Transportation that will be an integral part of our growing team. The Sr. Engineer, Transportation is responsible for identifying, recommending and implementing improvement opportunities across the supply chain, including distribution centers, transportation and logistics. Provide local technical engineering support to the operations teams in the distribution centers in the areas of labor management, operational processes, facility design and layout, equipment, automation and measuring efficiency, quality and effectiveness in the operations and transportation. Results should improve productivity across the operations and reduce the cost per unit, increase speed of distribution of goods and help accuracy. The RealReal is the world's largest online marketplace for authenticated, resale luxury goods, with more than 20 million members.