Artificial intelligence (AI) has been making serious technical progress over the last several years, but not in the political sense. Tech giants like Microsoft and Google, and online retailers like Amazon, have found new ways to accelerate their products using AI-driven algorithms. AI isn't exactly the correct term, however -- at least not in the sense that general consumers know it, in regard to machines like HAL from 2001: A Space Odyssey or Skynet in the Terminator movies. There's a difference between AI and Machine Learning, but it's such a new concept for the zeitgeist, the two are easily confused. AI is a broad term encompassing technology that employs advanced computer intelligence, but it's Machine Learning (ML) that really gives computers that human-like intellect seen in science fiction.
GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this post, we focus on general techniques for improving I/O to optimize GPU performance when training on Amazon SageMaker, regardless of the underlying infrastructure or deep learning framework. You can typically see performance improvements up to 10-fold in overall GPU training by just optimizing I/O processing routines. A single GPU can perform tera floating point operations per second (TFLOPS), which allows them to perform operations 10–1,000 times faster than CPUs.
As a data scientist attempting to solve a problem using supervised learning, you usually need a high-quality labeled dataset before starting your model building. Amazon SageMaker Ground Truth makes dataset building for a different range of tasks, like text classification and object detection, easier and more accessible to everyone. Ground Truth also helps you build datasets for custom user-defined tasks that let you annotate anything. For complex labeling tasks, such as complex taxonomy classification, extreme multi-class classifications, or autonomous driving labeling tasks, you may need to build a more complex front-end application for your labeling workforce. Front-end frameworks like Angular are helpful in these cases because they bring useful design patterns like model-view-controller (MVC), which makes your codebase more robust and maintainable for a larger team composed of UX/UI designers and software developers.
Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.
About Blue Ridge Blue Ridge Supply Chain Planning and Price Optimization solutions empower distributors and retailers to tap into undiscovered margin through enterprise-wide inventory intelligence, automation and synchronization. Blue Ridge uniquely combines demand forecasting with pricing strategy, so that businesses can proactively understand the unpredictable and allocate the right inventory – right-priced across the entire mix – to accelerate top- and bottom-line results. In a world where the only constant is change, Blue Ridge provides more certainty, more speed, and more assurance – so companies can see the why behind the buy, and respond faster to the unexpected. That's why major retailers and distributors rely on Blue Ridge for a more foreseeable future. For more information, go to www.blueridgeglobal.com.
LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.
Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in all Regions. This capability is available out-of-the-box and comes with the reticulate library pre-installed. This library offers an R interface for the Amazon SageMaker Python SDK, which enables you to invoke Python modules from within an R script.
With rapid advancements in machine learning (ML) techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. A study conducted by PwC Global concludes that by the end of this decade, the total positive impact of artificial intelligence (AI) on the global economy could be above $15 trillion, driven mostly by enhancements in consumer products. To make that happen, however, businesses must make strategic investments in the type of technology that moves AI projects into production (productionizing) and helps customers deploy them. Unfortunately, PwC's survey reveals the percentage of executives planning to deploy AI has gone down from 20 percent a year ago to only 4 percent at the beginning of 2020. The primary reason for this decrease is the gap between the growing volume of data and data-driven modeling capabilities, and the necessary skills and toolsets.
AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The purpose for these real-time analytics was to provide context and more in-depth experience with top competitors' strategies and tactics. This helped viewers tangibly interpret how specific model strategy translated to on-track performance, which further demystified ML development and demonstrated its real-world application. This enhancement enabled fans to monitor the performance and driving style of competitors from around the world.
Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thomson Reuters, use Amazon SageMaker to remove the heavy lifting from the ML process. With Amazon SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon CloudWatch, enable autoscaling to automatically scale endpoints based on traffic, and update your models in production without losing any availability. In many cases, such as e-commerce applications, offline model evaluation isn't sufficient, and you need to A/B test models in production before making the decision of updating models.