Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues. AWS Lambda is a serverless compute service that can provide real-time ML inference at scale. In this post, we demonstrate a sample data capture feature that can be deployed to a Lambda ML inference workload.

Almost every known large industry today produces and distributes goods at scale over a supply chain. In a typical supply chain, as a product travels across the network of supply chain nodes–from the producer, to the intermediate nodes (e.g pre-processor, distributor etc.), all the way to the end consumer–each node adds'value' to the product until it is ready to be sold off at the final node (e.g. retail stores). Supply chains are customarily designed and operated to minimise costs or maximise profits (or both). One of the most important considerations is to decide'when' and'how' much to replenish each product at each node of the supply chain, as it proceeds from production to consumption. While this customary approach works well for products that'do not perish' or have an unlimited shelf life, a straightforward extension of this approach for perishable products (e.g.

Instance-level image retrieval in fashion is a challenging issue owing to its increasing importance in real-scenario visual fashion search. Cross-domain fashion retrieval aims to match the unconstrained customer images as queries for photographs provided by retailers; however, it is a difficult task due to a wide range of consumer-to-shop (C2S) domain discrepancies and also considering that clothing image is vulnerable to various non-rigid deformations. To this end, we propose a novel multi-scale and multi-granularity feature learning network (MMFL-Net), which can jointly learn global-local aggregation feature representations of clothing images in a unified framework, aiming to train a cross-domain model for C2S fashion visual similarity. First, a new semantic-spatial feature fusion part is designed to bridge the semantic-spatial gap by applying top-down and bottom-up bidirectional multi-scale feature fusion. Next, a multi-branch deep network architecture is introduced to capture global salient, part-informed, and local detailed information, and extracting robust and discrimination feature embedding by integrating the similarity learning of coarse-to-fine embedding with the multiple granularities. Finally, the improved trihard loss, center loss, and multi-task classification loss are adopted for our MMFL-Net, which can jointly optimize intra-class and inter-class distance and thus explicitly improve intra-class compactness and inter-class discriminability between its visual representations for feature learning. Furthermore, our proposed model also combines the multi-task attribute recognition and classification module with multi-label semantic attributes and product ID labels. Experimental results demonstrate that our proposed MMFL-Net achieves significant improvement over the state-of-the-art methods on the two datasets, DeepFashion-C2S and Street2Shop. Specifically, our approach exceeds the current best method by a large margin of +4.2% and +11.4% for mAP and Acc@1, respectively, on the most challenging dataset DeepFashion-C2S.

Amazon, Netflix, and Indeed don't simply provide more options than traditional retail stores, video rental stores, and newspapers -- they provide so many more options that the human mind can effectively comprehend and parse. Users need to be shown what will most appeal to them. There were recommender systems before deep learning, but until that advancement, technical constraints ensured choice remained tyrannical. Deep learning has become an essential component of recommender systems, and anyone who wants to understand the latter must understand the former. Traditional recommender systems make recommendations to users based on previous user interactions or attributes, depending on whether the recommender system uses content-based filtering, collaborative filtering, or a hybrid of the two. Content-based filtering recommends items with similar features to items a user interacted with in the past.

For the 2022 holiday season, returnable items purchased between October 11 and December 25, 2022 can be returned until January 31, 2023. You may receive a partial or no refund on used, damaged or materially different returns.

In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. In this post, we highlight challenges commonly reported specifically in DL training, and how the open-source library MosaicML Composer helps solve them. DL models are trained iteratively, in a nested for loop. A loop iterates through the training dataset chunk by chunk and, if necessary, this loop is repeated several times over the whole dataset.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Michigan woman is being charged after allegedly stealing items from Walmart by not scanning all of her items at the self-checkout. Police say 34-year-old TeddyJo Marie Fliam was using the self-checkout at an Alpena County Walmart when loss prevention workers noticed she wasn't scanning every item. Fliam became agitated and denied she was skipping items when confronted by a loss prevention worker and left the store, according to a report from Fox 2. The incident caused the store to review its surveillance footage, which showed Fliam had stolen more than $1,000 in merchandise by not scanning it from the Walmart since April.

If you missed the chance to pick up an Echo during Amazon's recent Prime Day sales event, the retailer has discounted the smart speaker to its lowest price ever. This weekend, you can buy the Echo for $50, or half off its usual $100 price. We gave Amazon's spherical smart speaker a score of 89 when it came out in 2020. Since then, it has remained one of our favorites in the category. The Echo sounds great for its small size, outperforming similarly priced smart speakers like the Nest Audio and HomePod mini.

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. This work has been discussed in White House reports, Congressional testimony, European Commission documents, the Economist, the Globe and Mail, National Public Radio, the Atlantic, the New York Times, the Financial Times, the Wall Street Journal, and elsewhere. He holds a PhD in economics of Northwestern University.

Any news article, book, or film that predicts the world's future, a utopian or dystopian one, always has one thing in common -- the dominance of technology. Now more than ever, that future seems real and possible with the rise of artificial intelligence. On my podcast Figuring Out, I spoke to Srikanth Velamakanni, the founder of Fractal Analytics, a leading player in the artificial intelligence and transformative enterprise decision-making space. We discussed all things tech and more. As anticipated, AI (artificial intelligence) dominated the conversation to a large extent, which got me thinking about its impact on the employment market.