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.
Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on. Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don't. Stitch Fix's version of this service is called Fixes.
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.
Those who want to outfit a room or two with smart displays can get a couple of Amazon's smaller Echo Shows for less at HSN. The online retailer has a bundle that includes one Echo Show 5 and one Echo Show 8 for $140, which is a great price and close to the sale prices we saw for both of those devices back in May. If you were to buy each smart speaker separately right now, you'd spend $170 -- and that's with both the Show 5 and the Show 8 technically being on sale. In May, Amazon dropped the prices of both smart speakers to their Black Friday lows. If you had purchased one of each then, you would have paid $130.
The sense of discovery and surprise at something that matches an unrecognized yearning can be exhilarating. But the sheer size of many stores is exhausting -- with some, I need a site map, flashlight, and overnight bag to take it all in. In others, the inventory is specialized and packed in so tightly that online seems a better way to find what I want. If you're primarily an online shopper, you may be asking how relevant brick-and-mortar stores are. The answer is: quite relevant.
The central premise of this book is that value at the enterprise is created by making decisions, not with data or predictive technologies alone. Nonetheless, we can piggyback on the big data and AI revolutions and start making better choices in a systematic and scalable way, by transforming our companies into modern AI- and data-driven decision-making enterprises. To make better decisions, we first need to ask the right questions, forcing us to move from descriptive & predictive analyses to prescriptive courses of action. I devote the first few chapters to clarifying these concepts and explaining how to ask better business questions suitable for this type of analysis. I then delve into the anatomy of decision-making, starting with the consequences or outcomes we want to achieve, moving backward to the actions we can take, and discussing the problems and opportunities created by intervening uncertainty and causality.
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.
Microsoft Corp. said it won't sell facial-recognition technology to U.S. police until there is a national law regulating its use, echoing similar commitments from Amazon.com Inc. and International Business Machines Corp. made this week. The trio of technology companies have called for clearer federal rules around the surveillance technology amid widespread concern about its potential for racial bias. Meanwhile, the popular fantasy card game, "Magic: The Gathering," removed several cards it deemed racist or culturally offensive from its database, including one depicting figures in pointed hoods. The Hasbro-subsidiary behind the game also pledged to review all cards for material deemed inappropriate. The moves are the latest public actions by businesses lining up to show their commitment to racial equality.