While it may seem I'm just trying to work in as many buzzwords as I can, in fact, there really is an important intersection of these three elements. I've been interested in both big data and fast data for several years, and my newest tech interest is machine learning. As I have learned about the latter, I see that there are problems that require all three to be truly effective. One application for which I'm looking at bringing together these technologies is in Recommender Systems for brick and mortar shops. Probably the first big win for machine learning was Recommender Systems.
This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).
Petropoulos, Fotios, Apiletti, Daniele, Assimakopoulos, Vassilios, Babai, Mohamed Zied, Barrow, Devon K., Taieb, Souhaib Ben, Bergmeir, Christoph, Bessa, Ricardo J., Bijak, Jakub, Boylan, John E., Browell, Jethro, Carnevale, Claudio, Castle, Jennifer L., Cirillo, Pasquale, Clements, Michael P., Cordeiro, Clara, Oliveira, Fernando Luiz Cyrino, De Baets, Shari, Dokumentov, Alexander, Ellison, Joanne, Fiszeder, Piotr, Franses, Philip Hans, Frazier, David T., Gilliland, Michael, Gönül, M. Sinan, Goodwin, Paul, Grossi, Luigi, Grushka-Cockayne, Yael, Guidolin, Mariangela, Guidolin, Massimo, Gunter, Ulrich, Guo, Xiaojia, Guseo, Renato, Harvey, Nigel, Hendry, David F., Hollyman, Ross, Januschowski, Tim, Jeon, Jooyoung, Jose, Victor Richmond R., Kang, Yanfei, Koehler, Anne B., Kolassa, Stephan, Kourentzes, Nikolaos, Leva, Sonia, Li, Feng, Litsiou, Konstantia, Makridakis, Spyros, Martin, Gael M., Martinez, Andrew B., Meeran, Sheik, Modis, Theodore, Nikolopoulos, Konstantinos, Önkal, Dilek, Paccagnini, Alessia, Panagiotelis, Anastasios, Panapakidis, Ioannis, Pavía, Jose M., Pedio, Manuela, Pedregal, Diego J., Pinson, Pierre, Ramos, Patrícia, Rapach, David E., Reade, J. James, Rostami-Tabar, Bahman, Rubaszek, Michał, Sermpinis, Georgios, Shang, Han Lin, Spiliotis, Evangelos, Syntetos, Aris A., Talagala, Priyanga Dilini, Talagala, Thiyanga S., Tashman, Len, Thomakos, Dimitrios, Thorarinsdottir, Thordis, Todini, Ezio, Arenas, Juan Ramón Trapero, Wang, Xiaoqian, Winkler, Robert L., Yusupova, Alisa, Ziel, Florian
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Python's popularity is growing tremendously and it's becoming more and more relevant economically and technologically. In this 7 in 1 version you get a full collection of The Python Bible series. From the first volume on, you will be lead on a structured way to the mastery of Python. Besides the basics and the intermediate concepts, you will also learn how to apply it in areas like machine learning, financial analysis and neural networks. At the end you will additionally be introduced to one of the most interesting fields of computer science, which is computer vision After reading this collection, you will not only understand the programming language but you will also be able to work on projects in the stated fields.
Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.
With the increase of order fulfillment options and business objectives taken into consideration in the deciding process, order fulfillment deciding is becoming more and more complex. For example, with the advent of ship from store retailers now have many more fulfillment nodes to consider, and it is now common to take into account many and varied business goals in making fulfillment decisions. With increasing complexity, efficiency of the deciding process can become a real concern. Finding the optimal fulfillment assignments among all possible ones may be too costly to do for every order especially during peak times. In this work, we explore the possibility of exploiting regularity in the fulfillment decision process to reduce the burden on the deciding system. By using data mining we aim to find patterns in past fulfillment decisions that can be used to efficiently predict most likely assignments for future decisions. Essentially, those assignments that can be predicted with high confidence can be used to shortcut, or bypass, the expensive deciding process, or else a set of most likely assignments can be used for shortlisting -- sending a much smaller set of candidates for consideration by the fulfillment deciding system.
Artificial intelligence and machine learning (AI/ML) have made inroads into enterprises for a variety of different uses, including decision support, product recommendations and process control. These fields are employing big-data concepts to train software algorithms to evaluate data and respond in a similar manner to human decision-makers. These systems are boosted by data collected in the problem domain and used to successively adjust the algorithms to model that domain. For example, a retailer might use detailed data on sales experiences to recommend additional products for shoppers to purchase. By correlating purchases made by past customers, the retailer may be able to entice shoppers to make larger purchases than they had originally intended.
Throughout the global pandemic, people in every step of life were forced to interact with and rely on technology in new ways. Older generations adopted new habits like online grocery shopping, businesses quickly shifted to virtual meetings, and processes like vaccine distribution required a collaborative use of both AI and mass notification technology across all levels of government and industry. These experiences demonstrated how the intentional use of advanced technology can help to improve the lives and well-being of people during times of volatility. Particularly in the last 18 months, an unprecedented level of instability has upended business as usual. As a result, organizations of all kinds are now preparing for unpredictability more than ever before and are turning to modern technology, such as AI and big data, to help them manage these uncharted waters.
When you deploy intelligent search in your organization, two important factors to consider are access to the latest and most comprehensive information, and a contextual discovery mechanism. Many companies are still struggling to make their internal documents searchable in a way that allows employees to get relevant information knowledge in a scalable, cost-effective manner. A 2018 International Data Corporation (IDC) study found that data professionals are losing 50% of their time every week--30% searching for, governing, and preparing data, plus 20% duplicating work. Amazon Kendra is purpose-built for addressing these challenges. Amazon Kendra is an intelligent search service that uses deep learning and reading comprehension to deliver more accurate search results.