Uday Kamath has more than 20 years of experience architecting and building analytics-based commercial solutions. He currently works as the Chief Analytics Officer at Digital Reasoning, one of the leading companies in AI for NLP and Speech Recognition, heading the Applied Machine Learning research group. Most recently, Uday served as the Chief Data Scientist at BAE Systems Applied Intelligence, building machine learning products and solutions for the financial industry, focused on fraud, compliance, and cybersecurity. Uday has previously authored many books on machine learning such as Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified and Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures. Uday has published many academic papers in different machine learning journals and conferences.
A look at how Zulily is using the latest tools in artificial intelligence, machine learning, and cloud computing to innovate and serve its customers with purpose. Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience.
"Alternative data and machine learning are about to become essential components of the modern investment process. This excellent book offers practitioners a rich collection of case studies written by some of the most capable quants in the world today. It will be on our shelves here at Quandl for sure." "Tony Guida has managed to cover an impressive list of recent topics in Financial Machine Learning and Big Data, such as deep learning, reinforcement learning or natural language processing, in this book. It is accessible and rich with real-world applications, written in readable style. It will appeal to quants, students and regulators at all levels, and will undoubtedly become a reference textbook, one of the few not to be missed by anybody interested in Machine Learning and Big Data applications."
Technology has played a key role in retail for decades, from early innovations like barcode scanning and digital point of sale devices, to the global frontier of modern logistics. Through it all, however, the fundamentals remain the same: retailers generate huge quantities of data, face unpredictable environments, and need to continually adapt to the ever-evolving needs of the customer. Throw in the chaos of Black Friday and Cyber Monday, and you've got one of the most complex enterprise challenges in the world. It's also a challenge tailor-made for AI: a technology that thrives on big data, adapts to change fluidly, and can deliver personalized experiences at scale. With the holiday rush upon us, let's take a look at how two Cloud AI customers--3PM for online shoppers and Tulip for in-store--are helping make retail more efficient, more personal, and more trustworthy.
With the festive time of the year, everyone is going crazy about the mind-blowing sales going on in Flipkart Big Billion Day and Amazon India Great India Festival Sale. Diwali is celebrated to mark the triumph of Lord Rama over Ravana or the triumph of good over evil. However, for businesses in India, Diwali is the go-to make money festival. It is known as the big Indian Festive Season with eye-popping deals on from apparel, add-ons, shoes, consumer electronics, home appliances, furniture and also traveling. But did we ever imagined, how these e-commerce companies have prepared for these big sale days or how their courier partners are ensuring that last-mile delivery to the customers?
As brick-and-mortar retailers continue to struggle against online competitors, some are seeking out services that leverage big data and personalization to increase e-commerce sales. "During the rise of big data, it was said that data was the new oil," Brian Solis, principal analyst at Altimeter, told TechRepublic. "In an era of AI and machine learning however, personalized data is the new competitive advantage and will only become standard CX on the horizon." Indeed, 72% of retailers reported that AI will be a "competitive necessity" in the next five years, according to a recent Oxford Economics survey. One such tech option for retailers looking to fight off the competition is uSizy, a recommendation technology for fashion apparel and footwear businesses, which unveiled its latest product, uSizy Smart Business, on Wednesday.
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions. Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.
Even though Walmart was founded in 1962, it's on the cutting edge when it comes to transforming retail operations and customer experience by using machine learning, the Internet of Things (IoT) and Big Data. In recent years, its patent applications, position as the second largest online retailer and investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers. Lauren Desegur, VP of customer experience engineering at WalmartLabs said, "We're essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores." While its arch nemesis in business may be Amazon.com,