You know the Travelling Salesman Problem? Find the shortest route that takes you to every city on a list and returns you home. In fact it's an NP-hard problem, where NP stands for non-deterministic polynomial time. Just in case you were in any doubt about how hard it is. But if you're a grocery retailer, delivering the weekly shopping to millions of homes, or the country's leading furniture maker... well, it's a problem you have to solve.

Market research firm IDC released a report in August 2020 forecasting that the global AI market, composed of software, hardware, and services, is expected to total $156.5 billion in 2020, a 12.3% increase from 2019. Although the COVID-19 pandemic slightly slowed the usual growth, the firm believes that the AI market will recover quickly and projected its value to double in just four years, reaching over $300 billion by 2024. While the speed of AI adoption has varied across different industries, retail is one segment that has been slower to incorporate AI capabilities for a variety of reasons, including concerns over data privacy, job loss, and a lack of readiness. But with the recent ecommerce boom (largely driven by the pandemic), more retailers and "etailers" are finally taking a chance and exploring tech capabilities to create a more enjoyable, efficient, and tailored online shopping experience. Some brands, like ASOS, Burberry, and Victoria's Secret, have successfully deployed AI-driven chatbots to improve the customer support experience for their online shoppers.

New York, NY, May 26, 2021 (GLOBE NEWSWIRE) -- Facts and Factors have published a new research report titled "Artificial Intelligence in Retail Market By Type (Offline, and Online), By Technology (Natural Language Processing, Machine Learning, and Deep Learning, and Others), By Solution (Customer Relationship Management, Payment Services management, Price Optimization, Product Recommendation, and Planning, Supply chain management and Demand Planning, Virtual Assistant, Visual Search, Others) By Service (Managed Services, and Professional Services), By Deployment Model (On-Premises, and Cloud), and By Application (In-Store Visual Monitoring and Surveillance, Location-Based Marketing, Market Forecasting, Predictive Merchandising, Programmatic Advertising, and Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2020 – 2026". "According to the research report, the global Artificial Intelligence in Retail Market was estimated at USD 2.7 Billion in 2019 and is expected to reach USD 20.05 Billion by 2026. The global Artificial Intelligence in Retail Market is expected to grow at a compound annual growth rate (CAGR) of 39% from 2020 to 2026". Digitalization in retail is much more than just linking objects. It's about turning data into observations that guide decisions that produce better market results.

Artificial intelligence (AI) has been a game-changer for the retail and E-commerce industries. According to Statista, retail sales are projected to amount to around $30 trillion by 2023. According to Nasdaq, 95% of purchases will be facilitated by E-commerce by 2040. No doubt, AI will be shaping retail digitization. AI has pretty much to offer the retail industry.

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, training, tuning, hosting, explainability, monitoring, and workflow automation. Companies are increasingly training ML models based on individual user data. For example, an image sharing service designed to enable discovery of information on the internet trains custom models based on each user's uploaded images and browsing history to personalize recommendations for that user. The company can also train custom models based on search topics for recommending images per topic.

We design and build the systems with technology that powers ocado.com, the world's largest online-only grocery retailer; groceries.morrisons.com, the fastest growing online supermarket, and other clients. We are constantly pushing the limits of what our technology can do. To accelerate this work, we are expanding our development team in Sofia, Bulgaria and opening new roles. As our business evolves, we are writing a next generation, cloud based grocery platform, Ocado Smart Platform, which will be used to run Ocado, Morrisons, Kroger, ICA, Aeon, Coles and other international retailers in the future. Join us and you'll have the opportunity to work across a wide range of high class technology, with exceptionally smart and collaborative people, to create an unrivalled platform.

We're about a week away from the AWS Machine Learning Summit and if you haven't registered yet, you better get on it! On June 2, 2021 (Americas) and June 3, 2021 (Asia-Pacific, Japan, Europe, Middle East, and Africa), don't miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit. This Summit, which is open to all, brings together industry luminaries, AWS customers, and leading ML experts to share the latest in ML. You'll learn about science breakthroughs in ML, how ML is impacting business, best practices in building ML, and how to get started now without prior ML expertise. This post is your guide to navigating the Summit.

Deploying this solution and running the notebook builds the following environment in the AWS Cloud. The AWS CloudFormation template deploys an example dataset of credit card transactions contained in an Amazon Simple Storage Service (Amazon S3) bucket and an Amazon SageMaker notebook instance with different ML models that will be trained on the dataset. The solution also deploys an AWS Lambda function that processes transactions from the example dataset and invokes the two SageMaker endpoints that assign anomaly scores and classification scores to incoming data points. An Amazon API Gateway REST API triggers predictions using signed HTTP requests, and an Amazon Kinesis Data Firehose delivery stream loads the processed transactions into another Amazon S3 bucket for storage. The solution also provides an example of how to invoke the prediction REST API as part of the Amazon SageMaker notebook.

Machine learning (ML) is one of the most disruptive technologies we will encounter in our generation, but we're just getting started. The Machine Learning Summit brings together industry-leading scientists, AWS customers, and experts to dive deep in to the art, science, and impact of machine learning. You'll hear from industry leaders on the latest science breakthroughs in ML, get real-world learnings on how ML is impacting business, learn best practices in building ML, and learn how to get started with no expertise required. The Machine Learning Summit is free to attend, features over 30 sessions, and includes a live Q&A.

The push toward automation comes as businesses say they can't hire warehouse workers fast enough to meet surging online demand for everything from furniture to frozen food in pandemic-disrupted supply chains. The crunch is accelerating the adoption of robots and other technology in a sector that still largely relies on workers pulling carts. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. "This is not about taking over your job, it's about taking care of those jobs we can't fill," said Kristi Montgomery, vice president of innovation, research and development for Kenco Logistics Services LLC, a third-party logistics provider based in Chattanooga, Tenn. Kenco is rolling out a fleet of self-driving robots from Locus Robotics Corp. to bridge a labor gap by helping workers fill online orders at the company's largest e-commerce site, in Jeffersonville, Ind.