投稿時間:2021-08-18 07:28:50 RSSフィード2021-08-18 07:00 分まとめ(28件)

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TECH	Engadget Japanese	Boston Dynamics、人型ロボット「Atlas」出演の新たなパルクール動画を公開	https://japanese.engadget.com/atrals-robots-can-parkour-215005847.html	atlas	2021-08-17 21:50:05
TECH	Engadget Japanese	証拠を探して難事件を解決！タップ操作で楽しめる『ナゾトキ探偵社』：発掘！スマホゲーム	https://japanese.engadget.com/nazotoki-tantei-211052740.html	難事件	2021-08-17 21:10:52
TECH	Techable（テッカブル）	ハイパーカジュアルゲームが市場を牽引！ 2021年モバイルゲーム最新レポートが公開	https://techable.jp/archives/159972	appanniejapan	2021-08-17 21:00:25
Google	カグア！Google Analytics 活用塾:事例や使い方	【2021年版】YouTubeライブ配信前の設定画面でカスタマイズが実装され設定がしやすくなり、安全面も向上した	https://www.kagua.biz/social/youtube/20210818a1.html	youtube	2021-08-17 21:00:10
AWS	AWS - Webinar Channel	Amazon SageMaker for Healthcare and Life Sciences Use Cases - AWS Online Tech Talks	https://www.youtube.com/watch?v=sMOYZExnJRs	Amazon SageMaker for Healthcare and Life Sciences Use Cases AWS Online Tech TalksThe healthcare and life sciences HCLS industry faces mounting pressure to deliver more personalized treatments streamline processes modernize every aspect of the pharma value chain and keep patient information private and secure Machine learning ML addresses these challenges by automatically identifying anomalies in medical images building personalized healthcare treatment plans based on historical data and documents and identifying suspicious healthcare claims so HCLS organizations can offer higher quality more holistic treatment at lower costs In this tech talk you ll learn how Amazon SageMaker enables patients providers payers and researchers to prepare build train and deploy high quality ML models at lower cost and at scale Learning Objectives Discover challenges in the healthcare and life sciences industry and how ML can address key use cases such as fraud detection and anomaly detection Learn how SageMaker can help healthcare and life sciences companies prepare build train and deploy high quality ML models Walk through a demo to understand how to use SageMaker to automate medical diagnosis To learn more about the services featured in this talk please visit	2021-08-17 21:21:05
AWS	AWS - Webinar Channel	Invent New Experiences and Reimagine Old Processes with Machine Learning and Artificial Intelligence	https://www.youtube.com/watch?v=F3DbEfbWkgA	Invent New Experiences and Reimagine Old Processes with Machine Learning and Artificial IntelligenceAs part of your modern data strategy Machine Learning ML is one of the most disruptive technologies of our generation It can help create entirely new revenue opportunities make better and faster decisions or improve operational efficiencies In the fullness of time virtually every application will be infused with ML and Artificial Intelligence AI Come to this tech talk to hear how AWS meets customers where they are in their journey and helps them achieve specific business outcomes with the broadest and most complete set of ML and AI services for builders of all levels of expertise Learning Objectives Learn how a modern data strategy incorporates machine learning Learn about how machine learning can help enhance your customer experience improve productivity and optimize business processes or speed up and scale up innovation Learn about AWS machine learning solutions To learn more about the services featured in this talk please visit	2021-08-17 21:20:56
AWS	AWS - Webinar Channel	Beyond Overall Equipment Effectiveness (OEE) with Machine Vision and Virtual Andon	https://www.youtube.com/watch?v=3EQOmRUh-Hk	Beyond Overall Equipment Effectiveness OEE with Machine Vision and Virtual AndonIncreasingly manufacturers are leveraging new digital technologies to optimize their business processes speed innovation and lower costs In this tech talk you will discover how Overall Equipment Effectiveness OEE is only part of the picture in successful manufacturing Learn how augmenting industrial performance metrics from AWS IoT SiteWise with machine vision using Amazon Kinesis Video Streams and Amazon SageMaker Ground Truth to build highly accurate datasets for machine learning can deliver a virtual Andon system that gives factory workers the power to quickly and easily spot and report production issues Learning Objectives Learn how to collect organize and analyze data from industrial equipment Learn how to compute industrial performance metrics Discover how to identify production issues on the factory floor using AWS and AWS IoT services To learn more about the services featured in this talk please visit	2021-08-17 21:20:47
AWS	AWS - Webinar Channel	Migrating your VMware workloads to the cloud with VMware Cloud on AWS - AWS Virtual Workshop	https://www.youtube.com/watch?v=jjQzwBuluvs	Migrating your VMware workloads to the cloud with VMware Cloud on AWS AWS Virtual WorkshopOur customers have reduced their multi year migration plans down to months even weeks with VMware Cloud on AWS You can quickly migrate your workloads by leveraging existing VMware tools skill sets and governance across your on premises and cloud environments In this episode you will learn different ways to migrate your VMware workloads to the AWS Cloud Learning objectives Different migration options with VMware Cloud on AWS Migrating to virtual machines using vMotion and Hybrid Linked Mode Migrating to virtual machines using Hybrid Cloud Extension HCX	2021-08-17 21:18:09
js	JavaScriptタグが付けられた新着投稿 - Qiita	[React]フラグメントって？なんそれ！	https://qiita.com/ren0826jam/items/414b75e6e396b0913e69	lttablegtlttrgtltdivgtこのタグが余分lttdgtHellolttdgtlttdgtWorldlttdgtltdivgtこのタグが余分lttrgtlttablegt解決策以下のようにすることで、余分なノードを追加しなくても良くなります。	2021-08-18 06:00:48
海外TECH	Ars Technica	Updated app from Apple brings iCloud Passwords to Windows	https://arstechnica.com/?p=1787787	addition	2021-08-17 21:35:10
海外TECH	DEV Community	Top 7 Featured DEV Posts from the Past Week	https://dev.to/devteam/top-7-featured-dev-posts-from-the-past-week-27ma	Top Featured DEV Posts from the Past WeekEvery Tuesday we round up the previous week s top posts based on traffic engagement and a hint of editorial curation The typical week starts on Monday and ends on Sunday but don t worry we take into account posts that are published later in the week Improving the experience of a product with technical solutions michaelmangial explains how to stand out as a developer when collaborating with product teams The secret The more trusted you are the more valuable you are Not So Obvious Ways to Stand Out On a Product Team As a Developer Michael Mangialardi・Aug ・ min read career productivity webdev Navigating mapping Ever encountered the error field type is not supported for whatever you are trying to do with Elasticsearch lisahjung is here to help Beginner s guide Understanding mapping with Elasticsearch and Kibana Lisa・Aug ・ min read beginners elasticsearch database datascience Sometimes the fastest solution is ok dekel makes an argument for using jQuery for certain types of projects Last Week I Wrote Some jQuery and no one fired me Dekel・Aug ・ min read discuss javascript programming webdev The React learning curveCongrats to stuxnat for completing their final project for the Flatiron School Thanks for sharing it with us Final React Project Natalie Taktachev・Aug ・ min read reactnative redux javascript The ongoing journey of being a dev Learning how to code isn t the end of the journey for a software developer says bk So how can you continue to improve Read on These habits will make you a better developer Benjamin Kalungi・Aug ・ min read webdev productivity programming career Illustrating musicA few months back aneeqakhan created a boombox illustration by only using CSS In this post they share how they animated it Animating my illustration using animate css Aneeqa Khan ・Aug ・ min read react javascript css codenewbie Entities can not inject services salah explains that it s simple to implement a business rule in an entity method when the business logic only uses the properties of that entity But what if the business logic requires you to use any external services This post will help you navigate this conundrum Implementing Domain Driven Design Part III Salah Elhossiny・Aug ・ min read csharp dotnet That s it for our weekly wrap up Keep an eye on dev to this week for daily content and discussions and if you miss anything we ll be sure to recap it next Tuesday	2021-08-17 21:37:40
Apple	AppleInsider - Frontpage News	Apple releases watchOS 8 beta 6 to developers	https://appleinsider.com/articles/21/08/17/apple-releases-watchos-8-beta-6-to-developers?utm_medium=rss	Apple releases watchOS beta to developersApple s sixth developer beta for watchOS is now available for testing hours after beta versions of iOS iPadOS and tvOS were released on Tuesday watchOS developer beta is now available Read more	2021-08-17 21:59:27
Apple	AppleInsider - Frontpage News	Apple Watch Activity Challenge celebrates national parks on Aug. 28	https://appleinsider.com/articles/21/08/17/apple-watch-activity-challenge-celebrates-national-parks-on-aug-28?utm_medium=rss	Apple Watch Activity Challenge celebrates national parks on Aug Apple s next Apple Watch Activity Challenge is scheduled to kick off on Aug and will urge users to walk run hike and more in celebration of national parks Apple typically celebrates national parks in August and has over the past few years held an Apple Watch Activity Challenge to promote exploration of the great outdoors The challenge usually requires completion of a hike walk run or wheelchair workout of a mile or more to earn a special national parks themed badge This year s event is no different Read more	2021-08-17 21:43:54
海外TECH	Engadget	Intel is giving up on its AI-powered RealSense cameras	https://www.engadget.com/intel-realsense-shutdown-211525552.html?src=rss	Intel is giving up on its AI powered RealSense camerasIntel is pouring more and more of its energy into its mainstay chip business and that now means leaving some of its less essential work by the wayside The company told CRN in a statement that it was quot winding down quot RealSense and transferring the talent and computer vision tech to efforts that quot better support quot its core chip businesses The semiconductor giant will honor existing commitments but the end is clearly on the horizon Questions surfaced about the fate of RealSense after the team s leader Sagi Ben Moshe said he was leaving Intel two weeks ago RealSense aimed to make computer vision more flexible and accessible A company or researcher could buy cameras to aid everything from robot navigation through to facial recognition and there was even a developer focused phone It was never a truly mainstream product though and ASI VP Kent Tibbils told CRN that there were few customers buying RealSense cameras in any significant quantities It wasn t really a money making division even if the work helped Intel s other teams For Intel there s likely a simpler answer it wants to cut ballast CEO Pat Gelsinger wants Intel to reclaim the chipmaking crown and that means concentrating its resources on design and manufacturing capabilities No matter how successful RealSense is it s a potential distraction from Intel s latest strategy	2021-08-17 21:15:25
海外TECH	Engadget	YouTube Premium members can get three free months of Stadia Pro	https://www.engadget.com/google-stadia-pro-trial-youtube-premium-210431215.html?src=rss	YouTube Premium members can get three free months of Stadia ProGoogle has started a new promotion to entice YouTube Premium users to check out its Stadia Pro subscription Provided you re new to the paid service you can now get a three month trial to see if cloud gaming is your thing First spotted by toGoogle the promotion is only available to current YouTube Premium subscribers in the US Canada France Germany Hungary Netherlands Norway Poland Romania Spain Sweden Switzerland and the UK You can claim the three month trial until January st at which point you have until February th to redeem the offer According to the fine print on Google s website the promotion is also available to those who currently have a trial to YouTube Premium As a Stadia Pro subscriber you get access to several free titles every month You can also purchase select games and downloadable content at a discount But the reason most people get Stadia Pro is to stream the platform s catalog of games at a K resolution with HDR and surround sound	2021-08-17 21:04:31
海外TECH	Network World	5 things to know about pay-per-use hardware	https://www.networkworld.com/article/3629312/5-things-to-know-about-pay-per-use-hardware.html#tk.rss_all	things to know about pay per use hardware Pay per use hardware models such as HPE GreenLake and Dell Apex are designed to deliver cloud like pricing structures and flexible capacity to on premises data centers And interest is growing as enterprises look for alternatives to buying equipment outright for workloads that aren t a fit for public cloud environments The concept of pay per use hardware has been around for more than a decade but the buzz around it is growing said Daniel Bowers a former senior research director at Gartner “There s been a resurgence of interest in this for about four years driven a lot by HPE and its GreenLake program To read this article in full please click here	2021-08-17 21:14:00
Cisco	Cisco Blog	Email Security Recommendations You Should Consider from 2021	https://blogs.cisco.com/security/email-security-recommendations-you-should-consider-from-2021	Email Security Recommendations You Should Consider from We have put together recommendations for email security from trends in the current threat landscape customer user analysis the prevailing advice from analysts and our extensive experience in the market	2021-08-17 21:09:12
ニュース	BBC News - Home	UK to welcome 20,000 Afghans amid Taliban takeover	https://www.bbc.co.uk/news/uk-58250211	afghan	2021-08-17 21:29:09
ニュース	BBC News - Home	Carvalho shines as Championship leaders Fulham beat Millwall	https://www.bbc.co.uk/sport/football/58154318	championship	2021-08-17 21:31:28
ニュース	BBC News - Home	Bristol City win first game since March as Weimann double holds off Reading	https://www.bbc.co.uk/sport/football/58154322	reading	2021-08-17 21:45:42
ニュース	BBC News - Home	Raul Jimenez: Wolves striker on playing again following skull fracture	https://www.bbc.co.uk/sport/football/58250006	fracture	2021-08-17 21:37:26
LifeHuck	ライフハッカー［日本版］	メモを書き「ゴール・現在地・道のり」を意識すればすべてうまくいく	https://www.lifehacker.jp/2021/08/240380book_to_read-817.html	道のり	2021-08-18 07:00:00
北海道	北海道新聞	夏の甲子園大会は順延　高校野球、天候不良のため	https://www.hokkaido-np.co.jp/article/579250/	全国高校野球選手権大会	2021-08-18 06:17:00
北海道	北海道新聞	ＮＹ株反落、２８２ドル安　米景気回復の減速を懸念	https://www.hokkaido-np.co.jp/article/579243/	景気回復	2021-08-18 06:17:21
北海道	北海道新聞	コロナ禍の保健所に密着　貴重な記録、映画完成	https://www.hokkaido-np.co.jp/article/579249/	新型コロナウイルス	2021-08-18 06:17:00
北海道	北海道新聞	グーグル、５Ｇスマホの廉価版　１８日から予約、２６日発売	https://www.hokkaido-np.co.jp/article/579244/	通信システム	2021-08-18 06:07:00
GCP	Cloud Blog	Unlocking Application Modernization with Microservices and APIs	https://cloud.google.com/blog/products/application-modernization/app-modernization-with-googles-cloud-apigee-and-anthos/	Unlocking Application Modernization with Microservices and APIsIf you build apps and services that your customers consume two things are certain  You re exposing APIs in some form or the other  Your apps are made by multiple functions working together to deliver products and services  As you scale up and grow your enterprise architecture can benefit from a sound strategy for both API management and service management both of which impact your customer and developer experience In this article we ll explore how these two technologies fit into your application modernization strategy including how we re seeing our customers use Anthos Service Mesh and Apigee API Management together  How APIs microservices and a service mesh are relatedAPIs accelerate your modernization journey by unlocking and allowing legacy data and applications to be consumed by new cloud services As a result organizations can launch new mobile web and voice experiences for customers  The API layer acts as a buffer between legacy services and front end systems and keeps the front end systems up and running by routing requests as the legacy services are migrated or transformed into modern architectures  In addition an API management platform like Apigee manages the lifecycle of those APIs with design publish analyze and governance capabilities Once microservices architectures become prevalent in an organization technical complexity increases and organizations find a need for deeper and more granular visibility into their applications and services This is where a service mesh comes into play  A service mesh is not only an architecture that empowers managed observable and secure communication across an organization s services but also the tool that enables it Anthos Service Mesh lets organizations build platform scale microservices with requirements around standardized security policies and controls and it provides teams with in depth telemetry consistent monitoring and policies for properly setting and adhering to SLOs  How API management and a service mesh compliment one anotherMany organizations ask themselves “Do I really need both an API management platform and a service mesh How do I manage them together  The answer to the first question is yes These two technologies focus on different aspects of the technology stack and are complementary to each other A service mesh modernizes your application networking stack by standardizing how you deal with network security observability and traffic management An API management layer focuses on managing the lifecycle of APIs including publishing governance and usage analytics  Most organizations draw a logical boundary at business units or technology groups Sharing these microservices outside that boundary with other business units or with partners is where Apigee plays a significant role You can drive and manage the consumption of those services through developer portals monitoring API usage providing authentication and more with Apigee  Google Cloud offers Anthos Service Mesh for service management and Apigee for API management These two products work together to provide IT teams with a seamless experience throughout the application modernization journey The Apigee Adapter for Envoy enables organizations that use Anthos Service Mesh to reap the benefits of Apigee by enforcing API management policies within a service mesh  Accelerate your application modernization journeyThough the journey to application modernization doesn t always follow a clear cut path by adopting API management and a service mesh as part of a modernization journey your organization can be better equipped to rapidly respond to changing markets securely and at scale  Wherever you are on your application modernization journey Google Cloud can help To learn more about how service management and API management can be part of your application modernization journey read this whitepaper Related ArticleAnnouncing API management for services that use EnvoyAmong forward looking software developers Envoy has become ubiquitous as a high performance pluggable proxy providing improved networki Read Article	2021-08-17 22:00:00
GCP	Cloud Blog	Build a reinforcement learning recommendation application using Vertex AI	https://cloud.google.com/blog/topics/developers-practitioners/build-reinforcement-learning-recommendation-application-using-vertex-ai/	Build a reinforcement learning recommendation application using Vertex AIReinforcement learning RL is a form of machine learning whereby an agent takes actions in an environment to maximize a given objective a reward over this sequence of steps Applications of RL include learning based robotics autonomous vehicles and content serving The fundamental RL system includes many states corresponding actions and rewards to those actions Translate that into a movie recommender system The state is the user the action is the movie to recommend to the user and the reward is the user rating of the movie RL is a great framework for optimizing ML models as mentioned by Spotify in the keynote in the Applied ML Summit In this article we ll demonstrate an RL based movie recommender system executed in Vertex AI and built with TF Agents a library for RL in TensorFlow This demo has two parts a step by step guide leveraging Vertex Training Hyperparameter Tuning and Prediction services a MLOps guide to build end to end pipelines using Vertex Pipelines and other Vertex services TF Agents meets Vertex AIIn reinforcement learning RL an agent takes a sequence of actions in a given environment according to some policy with the goal of maximizing a given reward over this sequence of actions TF Agents is a powerful and flexible library enabling you to easily design implement and test RL applications It provides you with a comprehensive set of logical modules that support easy customization Policy A mapping from an environment observation to an action or a distribution over actions It is the artifact produced from training and the equivalent of a “Model in a supervised learning setup Action A move or behavior that is outputted by some policy and chosen and taken by an agent Agent An entity that encapsulates an algorithm to use one or more policies to choose and take actions and trains the policy Observations A characterization of the environment state Environment Definition of the RL problem to solve At each time step the environment generates an observation bears the effect of the agent action and then given the action taken and the observation the environment responds with a reward as feedback A typical RL training loop looks like the following A typical process to build evaluate and deploy RL applications would be Frame the problem While this blog post introduces a movie recommendation system you can use RL to solve a wide range of problems For instance you can easily solve a typical classification problem with RL where you can frame predicted classes as actions One example would be digit classification observations are digit images actions are predictions and rewards indicate whether the predictions match the ground truth digits Design and implement RL simulated experiments We will go into detail on simulated training data and prediction requests in the end to end pipeline demo Evaluate performance of the offline experiments Launch end to end production pipeline by replacing the simulation constituents with real world interactions Now that you know how we ll build a movie recommendation system with RL let s look at how we can use Vertex AI to run our RL application in the cloud We ll use the following Vertex AI products Vertex AI training to train a RL policy the counterpart of a model in supervised learning at scaleVertex AI hyperparameter tuning to find the best hyperparametersVertex AI prediction to serve trained policies at endpointsVertex Pipelines to automate monitor and govern your RL systems by orchestrating your workflow in a serverless manner and storing your workflow s artifacts using Vertex ML Metadata Step by step RL demoThis step by step demo showcases how to build the MovieLens recommendation system using TF Agents and Vertex AI services primarily custom training and hyperparameter tuning custom prediction and endpoint deployment This demo is available on Github including a step by step notebook and Python modules The demo first walks through the TF Agents on policy which is covered in detail in the demo training code of the RL system locally in the notebook environment It then shows how to integrate the TF Agents implementation with Vertex AI services It packages the training and hyperparameter tuning logic in a custom training hyperparameter tuning container and builds the container with Cloud Build With this container it executes remote training and hyperparameter tuning jobs using Vertex AI It also illustrates how to utilize the best hyperparameters learned from the hyperparameter tuning job during training as an optimization The demo also defines the prediction logic which takes in observations user vectors from prediction requests and outputs predicted actions movie items to recommend in a custom prediction container and builds the container with Cloud Build It deploys the trained policy to a Vertex AI endpoint and uses the prediction container as the serving container for the policy at the Vertex AI endpoint End to end workflow with a closed feedback loop Pipeline demoPipeline architectureBuilding upon our RL demo we ll now show you how to scale this workflow using Vertex Pipelines This pipeline demo showcases how to build an end to end MLOps pipeline for the MovieLens recommendation system using Kubeflow Pipelines KFP for authoring and Vertex Pipelines for orchestration Highlights of this end to end demo include RL specific implementation that handles RL modules training logic and trained policies as opposed to modelsSimulated training data simulated environment for predictions and re trainingClosing of the feedback loop from prediction results back to trainingCustomizable and reproducible KFP componentsAn illustration of the pipeline structure is shown in the figure below The pipeline consists of the following components Generator to generate MovieLens simulation data as the initial training dataset using a random data collecting policy and store in BigQuery executed only once Ingester to ingest training data in BigQuery and output TFRecord filesTrainer to perform off policy which is covered in detail in the demo training using the training dataset and output a trained RL policyDeployer to upload the trained policy create a Vertex AI endpoint and deploy the trained policy to the endpointIn addition to the above pipeline there are three components which utilize other GCP services Cloud Functions Cloud Scheduler Pub Sub Simulator to send recurring simulated MovieLens prediction requests to the endpointLogger to asynchronously log prediction inputs and results as new training data back to BigQuery per prediction requestsTrigger to recurrently execute re training on new training dataPipeline construction with Kubeflow Pipelines KFP You can author the pipeline using the individual components mentioned above The execution graph of the pipeline looks like the following Refer to the GitHub repo for detailed instructions on how to implement and test KFP components and how to run the pipeline with Vertex Pipelines Applying this demo to your own RL projects and productionYou can replace the MovieLens simulation environment with a real world environment where RL quantities like observations actions and rewards capture relevant aspects of said real world environment Based on whether you can interact with the real world in real time you may choose either on policy showcased by the step by step demo or off policy showcased by the pipeline demo training and evaluation If you were to implement a real world recommendation system here s what you d do You would represent users as some user vectors The individual entries in the user vectors may have actual meanings like age Alternatively they may be generated through a neural network as user embeddings Similarly you would define what an action is and what actions are possible likely all items available on your platform you would also define what the reward is such as whether the user has tried the item how long much the user has spent on the item user rating of the item and so on Again you have the flexibility to decide on representations for framing the problem that maximize performance During training or data pre collection you may randomly sample users and build the corresponding user vectors from the real world use those vectors as observations to query some policy for items to recommend and then apply that recommendation to users and obtain their feedback as rewards This RL demo can also be extended to ML applications other than recommendation system For instance if your use case is to build an image classification system then you can frame an environment where observations are the image pixels or embeddings actions are the predicted classes and rewards are feedback on the predictions correctness ConclusionCongratulations You have learned how to build reinforcement learning solutions using Vertex AI in a fully managed modularized and reproducible way There is so much you can achieve with RL and you now have many Vertex AI as well as Google Cloud services in your toolbox to support you in your RL endeavors be it production systems research or cool personal projects Additional resources Recap step by step demo link GitHub link Recap end to end pipeline demo GitHub linkTF Agents tutorial on bandits Introduction to Multi Armed BanditsVertex Pipelines tutorial Intro to Vertex PipelinesRelated ArticleUse Vertex Pipelines to build an AutoML classification end to end workflowHow you can use Vertex Pipelines to build an end to end ML workflow for training a custom model using AutoMLRead Article	2021-08-17 21:30:00