To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, or an AWS account. SageMaker Studio Lab resonates with customers who want to learn in either an informal or formal setting, as indicated by a recent survey that suggests 49% of our current customer base is learning on their own, whereas 21% is taking a formal ML class. Higher learning institutions have started to adopt it, because it helps them teach ML fundamentals beyond the notebook, like environment and resource management, which are critical areas for successful ML projects.

If you've been waiting for the holiday shopping season to grow your smart home ecosystem without dropping unnecessary amounts of money, you're in luck. Ahead of Black Friday, Amazon has already knocked down the price of the standard Ring Video Doorbell to $60. That's $15 cheaper than it was during Prime Day in July earlier this year and the best price we've seen it. There are a couple of compelling bundles you could get, too: pair the Video Doorbell with an Echo Show 5 for only $10 more, or get it with a battery-operated Ring Stick Up Camera for a total of $160. Amazon knocked 40 percent off the standard Ring Video Doorbell, bringing it down to the lowest price we've seen.

We propose a clustering procedure to group K populations into subgroups with the same dependence structure. The method is adapted to paired population and can be used with panel data. It relies on the differences between orthogonal projection coefficients of the K density copulas estimated from the K populations. Each cluster is then constituted by populations having significantly similar dependence structures. A recent test statistic from Ngounou-Bakam and Pommeret (2022) is used to construct automatically such clusters. The procedure is data driven and depends on the asymptotic level of the test. We illustrate our clustering algorithm via numerical studies and through two real datasets: a panel of financial datasets and insurance dataset of losses and allocated loss adjustment expense.

ALEXANDRIA, Va.--Innovation came to life for the Conexxus Innovation Research Committee (IRC) during a recent field trip that members made to multiple sites in Austin, Texas. One of the hallmarks of the IRC is to experience what's new for the industry firsthand. A visit to a new TXB Stores location in Georgetown, Texas, was on the list not only to taste its breakfast taco but also to see how an artificial intelligence pilot utilizing existing security camera system has progressed. Utilizing SparkCognition's Visual AI Advisor solution, the insights from the location visit were intriguing and revealing. To review the data, we visited with SparkCognition representatives at their offices and HyperWerx lab on a 50-acre site.

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You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. In 2013, after a decade in Silicon Valley, neuroscientist/designer duo Anand Chandrasekaran and Ashwini Asokan started Mad Street Den with the aim of taking computer vision technology it beyond the realm of scientific research. Today, through its Vue.ai business unit, the company helps retailers such as Diesel, Off-White and Tata CLiQ grow their businesses by reducing operational costs and increasing revenue through automation, and by creating personalized customer experiences. "Think of Vue.ai as a vertically integrated stack for the retail industry," said Asokan, who in addition to having co-founded of Mad Street Den serves as CEO of Vue.ai. "Today, a retailer has to shop across tens of vendors to avail a CDP, a recommendation system, a search engine, a styling and cross-sell solution, a marketing automation engine, A/B testing software, workflow automation -- the list is absolutely endless."

Automated Machine Learning (AutoML) is an upcoming field in machine learning (ML) that searches the candidate model space for a given task, dataset and an evaluation metric and returns the best performing model on the supplied dataset as per the given metric. AutoML not only reduces the manpower and expertise needed to develop ML models but also decreases the time-to-market for ML models substantially. We have designed an enterprise-scale AutoML framework called WALTS to meet the rising demand of employing ML in retail or any other business of interest, and thus help democratize ML within our organization. In this blog, we elaborate on how we explore models from a pool of candidates and underline how it has helped us with a business use-case. To give an overview of the AutoML process, its current landscape, and showcase the benefits of WALTS, we will be covering: · What is AutoML?

In model selection problems for machine learning, the desire for a well-performing model with meaningful structure is typically expressed through a regularized optimization problem. In many scenarios, however, the meaningful structure is specified in some discrete space, leading to difficult nonconvex optimization problems. In this paper, we connect the model selection problem with structure-promoting regularizers to submodular function minimization with continuous and discrete arguments. In particular, we leverage the theory of submodular functions to identify a class of these problems that can be solved exactly and efficiently with an agnostic combination of discrete and continuous optimization routines. We show how simple continuous or discrete constraints can also be handled for certain problem classes and extend these ideas to a robust optimization framework. We also show how some problems outside of this class can be embedded within the class, further extending the class of problems our framework can accommodate. Finally, we numerically validate our theoretical results with several proof-of-concept examples with synthetic and real-world data, comparing against state-of-the-art algorithms.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart.