Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text. This post is the second in a series on the new built-in algorithms in SageMaker. In the first post, we showed how SageMaker provides a built-in algorithm for image classification.

Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design and has published papers in EMNLP, ICLR, COLT, FOCS and SODA conferences. João Moura is an AI/ML Specialist Solutions Architect at Amazon Web Services. He is mostly focused on NLP use-cases and helping customers optimize deep learning model training and deployment. He is also an active proponent of low-code ML solutions and ML-specialized hardware. Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker built-in algorithms and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana Champaign. He is an active researcher in machine learning and statistical inference and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.

AI software development company, Boom Interactive, announced it has partnered with the Basha family, former owners of Bashas' Supermarkets, to bring its 3D design technology to the grocery and retail industries. "Boom Interactive's technology is changing the way commercial developers construct and design their spaces and how brands display their goods" After selling Bashas' to Raley's in 2021, brothers Preston, Dalton and Brandon Basha created a family office along with their other two siblings, Tortuga Trading Co., to invest in companies like Boom Interactive, to breathe new life into the grocery industry. Tortuga has committed to building out the retail version of Boom Interactive's flagship product, Bubbles, called R3D, and will be offering the software to their portfolio of retail partners. "Boom Interactive's technology is changing the way commercial developers construct and design their spaces and how brands display their goods," said Brandon Basha. "We are honored to get our hands dirty to help this revolutionary platform blossom."

Shin Dong-bin, the chairman of Lotte Group, and Tim Steiner, the CEO of Ocado Group, were both at the signing ceremony, which took place at Lotte World Tower in Seoul. Lotte Shopping announced that it has signed a partnership agreement with Ocado, a global retail technology company based in Britain, to enhance its online grocery solutions in Korea. The company will invest 1 trillion won ($705,060,000) by 2030 in "smart logistics" that focuses on fresh food to lead the 135 trillion won domestic online grocery market. Under the terms of the deal, Lotte Shopping will be able to use the Ocado Smart Platform (OSP) for all of its online shopping. OSP is a technology solution that covers the whole process of buying fresh food online, from attracting customers through mobile apps to delivering orders.

Three QUT researchers are part of an international research team that have identified new ways for retailers to use Artificial Intelligence in concert with in-store cameras to better capture consumer behavior and tailor store layouts to maximize sales. In research published in Artificial Intelligence Review, the team propose an AI-powered store layout design framework for retailers to best take advantage of recent advances in AI techniques, and its sub-fields in computer vision and deep learning to monitor the physical shopping behaviors of their customers. Any shopper who has retrieved milk from the farthest corner of a shop knows well that an efficient store layout presents its merchandise to both attract customer attention to items they had not intended to buy, increase browsing time, and easily find related or viable alternative products grouped together. A well-thought-out layout has been shown to positively correlate with increased sales and customer satisfaction. It is one of the most effective in-store marketing tactics that can directly influence customer decisions to boost profitability.

In research published in Artificial Intelligence Review, the team propose an AI-powered store layout design framework for retailers to best take advantage of recent advances in AI techniques, and its sub-fields in computer vision and deep learning to monitor the physical shopping behaviours of their customers. Any shopper who has retrieved milk from the farthest corner of a shop knows well that an efficient store layout presents its merchandise to both attract customer attention to items they had not intended to buy, increase browsing time, and easily find related or viable alternative products grouped together. A well thought out layout has been shown to positively correlate with increased sales and customer satisfaction. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost profitability. QUT researchers Dr Kien Nguyen and Professor Clinton Fookes from the School of Electrical Engineering & Robotics and Professor Brett Martin, QUT Business Schoolteamed up with researchers Dr Minh Le, from the University of Economics, Ho Chi Minh city, Vietnam, and Professor Ibrahim Cil from Sakarya University, Serdivan, Turkey, to conduct a comprehensive review on existing approaches to in store layout design.

Artificial Intelligence has been making game-changing impacts in the business world. Nowadays, businesses, especially retailers, regardless of size, are increasingly enthusiastic to build and deploy AI solutions. In fact, retail businesses must implement AI-like trending digital solutions to remain competitive and improve customer experiences. A few weeks ago, when FuGenX decided to add more services to its AI service portfolio, it has conducted a series of deep research across industry verticals to find which industries have more pain points that can be solved by our cutting-edge AI solutions. As per the research reports, retail was expectedly at the top of the list.

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Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with high availability while maximizing resource utilization and reducing associated costs. Because each ML framework has its own dependencies, and deployment steps for each framework are different, deploying models built in different frameworks in production and managing each of the endpoints becomes more and more complex. Amazon SageMaker multi-container endpoints (MCEs) enables us to group models on different frameworks and deploy them to the same host, creating a single endpoint. You can provide containers for the different frameworks that you're using to build the models, and SageMaker takes all of these containers and puts them behind one endpoint.

What can an AI tell you about Black Friday deals? Not much, it turns out, but when we posed a few contrarian thoughts about the upcoming shopping bacchanalia, AI image generator Dall-E 2 (opens in new tab) returned some interesting images that got us thinking about what this Black Friday and Cyber Monday shopping season will be like for consumers. I think it's a safe assumption that this won't be a typical Black Friday and pre-Christmas shopping season. We face extraordinary upheaval around the world, and a deepening cost of living crisis that in many countries is threatening to turn into a full-blown recession. Yet, recent studies point to consumers being both more financially cautious and, yet, more deeply invested in Black Friday than in recent years. During Amazon's Prime Day 2 (its new attempt to own the shopping season by preempting Black Friday), US consumers spent, according to Numerator (opens in new tab), on average $46.68, which is down roughly $15 from Amazon's mid-year Prime Day.