Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that wants to train a model using Pipelines to also preprocess training data, postprocess inference data, or evaluate models using PySpark. This capability is especially relevant when you need to process large-scale data.

AutoRevo, a leading automotive industry software provider, announces the launch of its groundbreaking AI Vehicle Description Builder, an innovative solution that addresses the challenges dealerships face in creating engaging, accurate, and consistent vehicle descriptions for their online inventory. With the AI Vehicle Description Builder, AutoRevo is set to revolutionize the way dealerships present their vehicles on digital platforms. The automotive industry has long struggled with the time-consuming and resource-intensive task of generating effective vehicle descriptions. Realizing that many dealerships either lack the resources for crafting detailed descriptions or struggle to maintain consistency, AutoRevo developed a cutting-edge AI tool designed to streamline the process and enhance online vehicle listings. The AI Vehicle Description Builder works in conjunction with inventory companies to produce high-quality, accurate, and engaging descriptions.

Kering, the world's second-largest luxury goods group after LVMH, has launched an experimental site called KNXT, which is a cutting-edge fashion space for curating innovative content and testing new ideas. The site incorporates both artificial intelligence (AI) and NFT tech, and is aimed at providing a unique shopping experience to visitors. One of the exciting features of KNXT is the introduction of an AI-powered chatbot named '/madeline', which is powered by OpenAI's ChatGPT, and is the first of its kind. This personal shopper is capable of recommending products from a variety of the group's brands, including Gucci, Bottega Venetia, Alexander McQueen, Balenciaga, and more. We are thrilled to introduce you to /madeline, the first AI personal shopper leveraging @OpenAI's #ChatGPT.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

With the advent of high-speed 5G mobile networks, enterprises are more easily positioned than ever with the opportunity to harness the convergence of telecommunications networks and the cloud. As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. Retailers can deliver more frictionless experiences on the go with natural language processing (NLP), real-time recommendation systems, and fraud detection. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

Massive transaction data sets are recorded in a routine manner in telecommunications, retail commerce, and Web site management. In this paper we address the problem of inferring predictive in- dividual proflles from such historical transaction data. We de- scribe a generative mixture model for count data and use an an approximate Bayesian estimation framework that efiectively com- bines an individual's speciflc history with more general population patterns. We use a large real-world retail transaction data set to illustrate how these proflles consistently outperform non-mixture and non-Bayesian techniques in predicting customer behavior in out-of-sample data.

Machine learning (ML) and artificial intelligence (AI) have burst into public consciousness in the last several years. While large language and multimodal models like GPT-4 have recently taken the excitement to a new level, developments in voice recognition software, novel recommendation systems for online retailers and streaming services, superhuman-level play by computers in Chess and Go, and unfulfilled promises in technologies like self-driving cars have been generating interest for more than a decade. Many research disciplines are feeling the profound effects of AI. For example, scientists can now utilize neural networks (NNs) to predict a protein's structure based on its amino acid sequence [3] -- a problem that was identified decades ago as a grand challenge for computational science. ML, AI, data science, data analysis, data mining, and statistical inference all have different but overlapping meanings; the term "data science" is perhaps the most general.

Covariant, an AI robotics company, has announced it has raised an additional $75 million in Series C funds, bringing its total funding to $222 million. Returning investors Radical Ventures and Index Ventures co-led the round, which also saw additional funding from returning investors Canada Pension Plan Investment Board and Amplify Partners. The round also welcomed new investors Gates Frontier Holdings, AIX Ventures, and Northgate Capital. The funding will be used to ensure today's leading retailers and their logistics providers are able to deploy robotic picking quickly and without disruption to their current operations, Covariant stated. This comes at a time when retail executives are eager to invest in AI-powered robotic automation: according to a Covariant-led research survey from February 2023, more than 80% of retail leaders see automation as a key solution for navigating operational uncertainty in an unpredictable marketplace – and 98% plan to further invest in AI Robotics in 2023 despite current economic conditions.

Customers shopping for a used car can sometimes feel overwhelmed digging through countless specs and reviews, but CarMax, the largest used car retailer in the U.S., is making it easier for customers to find the most useful information. Thanks to powerful AI language models, potential buyers can now see summaries of customer reviews for every make, model and year of vehicle that CarMax sells, about 5,000 combinations in a vast inventory of approximately 45,000 cars. The summaries provide easy-to-read takeaways from real customer reviews: whether it's a great family car, how comfortable the ride is or if there's enough space to pack for weekend adventures. CarMax has also used the models to create new website content that allows customers to easily see what's new for each version of a car, helping them decide whether new features are worth splurging on. CarMax generated the massive amount of original content in just a few months -- a rate previously impossible -- with powerful GPT-3 natural language models built by the company OpenAI.

Playing as Kurt, the sole human employee of an Amazon-like online retailer called Jüngle, you spend your days keeping pace with an army of robotic drones as they sort millions of packages for delivery. Then an activist group wrangles you into a scheme to bring down the giant corporation, whereupon both Kurt's world and the game's central premise begin to fall apart. Initially, The Last Worker is built around a light simulation of Kurt's daily routine. Using a hovering cart, you must locate assigned packages among the endless shelving units, and either transport them to a delivery chute or send them for recycling. Packages vary in size, weight, and condition, all of which must be checked before dispatch.