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.

H&M Group is leveraging AI to achieve a climate positive value chain by 2040. The clothing retailer uses AI-driven demand prediction to optimise the supply chain, said Linda Leopold, head of AI at H&M. Two hundred plus data scientists are working at H&M to understand purchasing patterns and trends across its stores. The company uses big data to analyse customer needs at a local level. The team has built algorithms to analyse store receipts, returns in the store, and loyalty-card data to study customer demands.

With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy with anomaly detection, help provide continuous improvements, and retrain models with updated predictions. However, you're often forced to choose between an ML-only or human-only system. Companies are looking for the best of both worlds, integrating ML systems into your workflow while keeping a human eye on the results to achieve higher precision.

In auto racing, a fast start is great, but endurance wins the race. As things inevitably go wrong during the race, the teams who win are those that can analyze and interpret all the data the car is sending back from the car and the pit and then make the right decisions. The winning team is the one proactively anticipating problems before they occur – making real-time adjustments to tip the scales in their favor. E-commerce is much the same. Online retailers cannot afford to leave things to chance.