投稿時間:2022-02-12 11:18:42 RSSフィード2022-02-12 11:00 分まとめ(23件)

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IT	気になる、記になる…	Spigen、ケース系全商品を最大14％オフで購入出来るクーポンを配布するバレンタインデーキャンペーンを開催中	https://taisy0.com/2022/02/12/151945.html	amazon	2022-02-12 01:43:21
IT	気になる、記になる…	新型｢Fire 7｣?? Amazonの新型タブレットがFCCの認証を通過 − 現行｢Fire HD 10｣の再申請の可能性も	https://taisy0.com/2022/02/12/151942.html	aftnews	2022-02-12 01:32:59
TECH	Engadget Japanese	ポケモンGO「ハネッコ」コミュニティ・デイ開催。色違い初登場、公園でアメXL増加の新仕様	https://japanese.engadget.com/pokemon-go-hoppip-cd-015337310.html	大量発生	2022-02-12 01:53:37
TECH	Engadget Japanese	2021年11月に亡くなった写真家・小原玲さん作品展の開催を支援する	https://japanese.engadget.com/rei-ohara-011017415.html		2022-02-12 01:10:17
ROBOT	ロボスタ	「トコトンやさしいサービスロボットの本」発売　家庭や職場での活用が広がっているサービスロボットを楽しく紹介	https://robotstart.info/2022/02/12/nikkan-service-robot-book.html	「トコトンやさしいサービスロボットの本」発売家庭や職場での活用が広がっているサービスロボットを楽しく紹介シェアツイートはてブ日刊工業新聞社から、初心者・初学者に好評の「今日からモノ知りシリーズ」の冊として、書籍『トコトンやさしいサービスロボットの本』が発売される。	2022-02-12 01:00:12
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 20～39歳に聞いた友達を作る場所　1位は「職場」、2位は？	https://www.itmedia.co.jp/business/articles/2202/11/news027.html	itmedia	2022-02-12 10:30:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 東京都23区外の「人気駅」を発表　3位「吉祥寺駅」、2位「三鷹駅」、1位は？	https://www.itmedia.co.jp/business/articles/2202/12/news020.html	itmedia	2022-02-12 10:30:00
python	Pythonタグが付けられた新着投稿 - Qiita	python:gpt-3を使ってAIを感じてみる。	https://qiita.com/be_tiger/items/24b755c28df4c26bfa23	内容①gptとは②活用例③実際に使ってみて①gtpとは・OpenAIの最新の深層学習モデルにアクセスすることができるクラウドサービス・触ったばかりで詳細は理解してませんが、「色んな文章作成サービス」と捉えています。	2022-02-12 10:47:14
js	JavaScriptタグが付けられた新着投稿 - Qiita	MicrosoftのWeb開発教材を使ってみた ⑤-2ブラウザ拡張機能 【Promise/API/LocalStorage/拡張機能作成/BackGround/Performance】	https://qiita.com/NasuPanda/items/4186e6701cd2d4d68bb2	具体的には、Promiseのような機能を利用することで、ある処理例サーバーからの画像の取得を実行し、その結果が返ってくるまで別の処理の実行を待たせる、といったことが可能。	2022-02-12 10:06:34
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Rails,Ruby】ページタイトルを三項演算子でシンプルに	https://qiita.com/miya114/items/c2e7f19a4877cc544185	【RailsRuby】ページタイトルを三項演算子でシンプルに三項演算子下記のように「」と「」を使って、ifelseendのくだりを一行で書ける書き方。	2022-02-12 10:04:02
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS GreengrassにComponentをデプロイする #2 (Raspberry PiからS3にファイルを共有する)	https://qiita.com/Buffalo46/items/04e4fbf8050b3fd13a07	簡単だしサービスを走らせるときだけにRoleを一時的に使うというのは、セキュリティの観点からも問題ないのではないかということで、これを採用。	2022-02-12 10:48:43
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails,Ruby】ページタイトルを三項演算子でシンプルに	https://qiita.com/miya114/items/c2e7f19a4877cc544185	【RailsRuby】ページタイトルを三項演算子でシンプルに三項演算子下記のように「」と「」を使って、ifelseendのくだりを一行で書ける書き方。	2022-02-12 10:04:02
技術ブログ	Developers.IO	My opinion of role configuration using dbt in Snowflake #dbt #SnowflakeDB	https://dev.classmethod.jp/articles/dbt-snowflake-my-roll-configuration-english/	My opinion of role configuration using dbt in Snowflake dbt SnowflakeDB This article is English ver of this article Hi I m Sagara In Japanese さがら dbt is a great match for bui	2022-02-12 01:24:42
海外TECH	DEV Community	Introduction to Amazon Machine Learning	https://dev.to/aws-builders/introduction-to-amazon-machine-learning-5adc	Introduction to Amazon Machine Learning IntroductionAWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure putting machine learning in the hands of every developer data scientist and expert practitioner When you build an ML based workload in AWS you can choose from three different levels of ML services to balance speed to market with level of customization and ML skill level Artificial Intelligence AI servicesML servicesML frameworks and infrastructureThe AI Services level provides fully managed services that enable you to quickly add ML capabilities to your workloads using API calls This gives you the ability to build powerful intelligent applications with capabilities such as computer vision speech natural language chatbots predictions and recommendations Services at this level are based on pre trained or automatically trained machine learning and deep learning models so you don t need ML knowledge to use them You can use Amazon Translate to translate or localize text contentAmazon Polly for text to speech conversionAmazon Lex for building conversational chat botsAmazon Comprehend to extract insights and relationships from unstructured dataAmazon Forecast to build accurate forecasting modelsAmazon Fraud Detector to identify potentially fraudulent online activities Amazon CodeGuru to automate code reviews and to identify most expansive lines of codeAmazon Textract to extract text and data from documents automaticallyAmazon Rekognition to add image and video analysis to your applicationsAmazon Kendra to reimagines enterprise search for your websites and applicationsAmazon Personalize for real time personalized recommendationsAmazon Transcribe to add speech to text capabilities to your applicationsThe ML Services level provides managed services and resources for machine learning to developers data scientists and researchers Amazon SageMaker enables developers and data scientists to quickly and easily build train and deploy ML models at any scale Amazon SageMaker Ground Truth helps you build highly accurate ML training datasets quickly Amazon SageMaker Studio is the first integrated development environment for machine learning to build train and deploy ML models at scale Amazon SageMaker Autopilot automatically builds trains and tunes the best ML models based on your data while enabling you to maintain full control and visibility Amazon SageMaker JumpStart helps you quickly and easily get started with ML Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes Amazon SageMaker Feature Store is a fully managed purpose built repository to store update retrieve and share ML features Amazon SageMaker Clarify provides ML developers with greater visibility into your training data and models so you can identify and limit bias and explain predictions Amazon SageMaker Debugger optimizes ML models with real time monitoring of training metrics and system resources Amazon SageMaker s distributed training libraries automatically split large deep learning models and training datasets across AWS graphics processing unit GPU instances in a fraction of the time it takes to do manually Amazon SageMaker Pipelines is the first purpose built easy to use continuous integration and continuous delivery CI CD service for ML Amazon SageMaker Neo enables developers to train ML models once and then run them anywhere in the cloud or at the edge IntroductionAmazon EC with instances acting as AWS virtual machines provides an ideal platform for operating your own self managed big data analytics applications on AWS infrastructure Almost any software you can install on Linux or Windows virtualized environments can be run on Amazon EC and you can use the pay as you go pricing model AWS Graviton processors are custom built by AWS using bit Arm Neoverse cores to deliver the best price performance for your cloud workloads running in Amazon EC Big Data Analytics Options on AWS is a Series containing different articles that provides a basic introduction to different Big Data Analytics Options on AWS Each article covers the detailed guide on how each service is used for collecting processing storing and analyzing big data Amazon EC provides the broadest and deepest portfolio of compute instances including many that are powered by latest generation Intel and AMD processors AWS Graviton processors add even more choice to help customers optimize performance and cost for their workloads What you don t get are the application level managed services that come with the other services mentioned in this whitepaper There are many options for self managed big data analytics A NoSQL offering such as MongoDBA data warehouse or columnar store like VerticaA Hadoop clusterAn Apache Storm clusterAn Apache Kafka environmentAny self managed big data workload that runs on EC can also run on an AWS fully managed container orchestration service such as Amazon ECS Amazon EKS and AWS Fargate Fargate is a serverless compute engine for containers that works with ECS and EKS Ideal usage patternsSpecialized environment When running a custom application a variation of a standard Hadoop set or an application not covered by another AWS offering Amazon EC provides the flexibility and scalability to meet your computing needs Compliance requirements Certain compliance requirements may require you to run applications yourself on Amazon EC instead of using a managed service offering Cost modelAmazon EC has a variety of instance types in a number of instance families standard high CPU high memory high I O and so on and different pricing options On Demand Compute Savings plan Reserved and Spot At the time of this writing when running applications on ECS you pay only for underlying EC instances with no additional charge for using ECS However for EKS you pay an additional per hour for each EKS cluster you have along with underlying EC instances AWS Fargate pricing is calculated based on the vCPU memory and storage resources used from the time you start to download your container image until the Amazon ECS task or Amazon EKS pod finishes rounded up to the nearest second While cost is dependent on various factors based on the use case Graviton instances have in general been able to provide better price performance over previous generation instances Depending on your application requirements you may want to use additional services along with Amazon EC EKS or ECS such as Amazon Elastic Block Store Amazon EBS for directly attached persistent storage or S as a durable object store each comes with its own pricing model If you do run your big data application on Amazon EC EKS or ECS you are responsible for any license fees just as you would be in your own data center The AWS Marketplace offers many different third party big data software packages pre configured to launch with a simple click of a button PerformancePerformance in Amazon EC EKS or ECS is driven by the instance type that you choose for your big data platform Each instance type has a different amount of CPU RAM storage IOPs and networking capability so that you can pick the right performance level for your application requirements Durability and availabilityCritical applications should be run in a cluster across multiple Availability Zones within an AWS Region so that any instance or data center failure does not affect application users For non uptime critical applications you can back up your application to Amazon S and restore to any Availability Zone in the Region if an instance or zone failure occurs Other options exist depending on which application you are running and the requirements such as mirroring your application Scalability and elasticityAuto Scaling is a service that enables you to automatically scale your Amazon EC capacity up or down according to conditions that you define With Auto Scaling you can ensure that the number of EC instances you re using scales up seamlessly during demand spikes to maintain performance and scales down automatically during demand lulls to minimize costs Auto Scaling is particularly well suited for applications that experience hourly daily or weekly variability in usage Auto Scaling is enabled by CloudWatch and available at no additional charge beyond CloudWatch fees InterfacesAmazon EC EKS and ECS can be managed programmatically via API SDK or the AWS Management Console Metrics for compute utilization memory utilization storage utilization network consumption and read write traffic to your instances are free of charge using the console or CloudWatch API operations The interfaces for your big data analytics software that you run on top of Amazon EC varies based on the characteristics of the software you choose Anti patternsAmazon EC has the following anti patterns Managed service If your requirement is a managed service offering where you abstract the infrastructure layer and administration from the big data analytics then this “do it yourself model of managing your own analytics software on Amazon EC may not be the correct choice Lack of expertise or resources If your organization does not have or does not want to expend the resources or expertise to install and manage a high availability installation for the system in question you should consider using the AWS equivalent such as Amazon EMR DynamoDB Amazon Kinesis Data Streams or Amazon Redshift Hope this guide gives you an Introduction to Amazon Compute Services Let me know your thoughts in the comment section And if you haven t yet make sure to follow me on below handles connect with me on LinkedInconnect with me on Twitter‍follow me on github️Do Checkout my blogs Like share and follow me for more content ltag user id follow action button background color important color fac important border color important Adit ModiFollow Cloud Engineer AWS Community Builder x AWS Certified x Azure Certified Author of Cloud Tech DailyDevOps amp BigDataJournal DEV moderator Reference NotesBig Data Analytics Options on AWS is a Series containing different articles that provides a basic introduction to different Big Data Analytics Options on AWS Each article covers the detailed guide on how each service is used for collecting processing storing and analyzing big data The ML Frameworks and Infrastructure level is intended for expert ML practitioners These people are comfortable with designing their own tools and workflows to build train tune and deploy models and are accustomed to working at the framework and infrastructure level In AWS you can use open source ML frameworks such as TensorFlow PyTorch and Apache MXNet The Deep Learning AMI and Deep Learning Containers in this level have multiple ML frameworks preinstalled that are optimized for performance This optimization means that they are always ready to be launched on powerful ML optimized compute infrastructure such as Amazon EC P and Pdn instances that provides a boost of speed and efficiency to ML workloads Amazon ML can create ML models based on data stored in S Amazon Redshift or Amazon RDS Built in wizards guide you through the steps ofinteractively exploring your data to training the ML model evaluate the model quality and adjust outputs to align with business goals After a model is ready you can request predictions in batches or using the low latency real time API Workloads often use services from multiple levels of the ML stack Depending on the business use case services and infrastructure from the different levels can be combined to satisfy multiple requirements and achieve multiple business goals For example you can use AI services for sentiment analysis of customer reviews on your retail website and use managed ML services to build a custom model using your own data to predict future sales Ideal usage patternsAmazon ML is ideal for discovering patterns in your data and using these patterns to create ML models that can generate predictions on new unseen data points For example you can Enable applications to flag suspicious transactions Build an ML model that predicts whether a new transaction is legitimate or fraudulent Forecast product demand Input historical order information to predict future order quantities Media intelligence Maximize the value of media content by adding machine learning to media workflows such as search and discovery content localization compliance monetization and more Personalize application content Predict which items a user will be most interested in and retrieve these predictions from your application in real time Predict user activity Analyze user behavior to customize your website and provide a better user experience Listen to social media Ingest and analyze social media feeds that potentially impact business decisions Intelligent contact center Enhance your customer service experience and reduce costs by integrating ML into your contact center Intelligent search Boost business productivity and customer satisfaction by delivering accurate and useful information faster from siloed and unstructured information sources across the organization Cost modelWith Amazon Machine Learning services you pay only for what you use There are no minimum fees and no upfront commitments AWS pre trained AI Services cost model varies depending upon the AI service you are planning to integrate with your applications For details see pricing details of the respective AI services Amazon ComprehendAmazon ForecastAmazon Fraud DetectorAmazon TranslateAmazon CodeGuruAmazon TextractAmazon RekognitionAmazon PollyAmazon LexAmazon KendraAmazon PersonalizeAmazon TranscribeWith Amazon SageMaker you have two choices to pay and you only pay for what you use On demand pricing is billed by the second with no minimum fees and no upfront commitments SageMaker Savings Plans offer a flexible usage based pricing model in exchange for a commitment to a consistent amount of usage For details see Amazon SageMaker pricing The ML Frameworks and Infrastructure level is intended for expert ML practitioners ML training and inference workloads can exhibit characteristics that are steady state such as hourly batch tagging of photos for a large population spiky such as kicking off new training jobs or search recommendations during promotional periods or both AWS has pricing options and solutions to help you optimize your infrastructure performance and costs For details see the AWS Machine Learning Infrastructure PerformanceThe time it takes to create models and to request predictions from ML models depends on the number of input data records and the types and distribution of attributes that you specify There are a number of principles designed to help increase performance specifically for ML workloads Optimize compute for your ML workload ーMost ML workloads are very compute intensive because large amounts of vector multiplications and additions need to be performed on a multitude of data and parameters Especially in Deep Learning there is a need to scale to chipsets that provide larger queue depth higher Arithmetic Logic Units and Register counts to allow for massively parallel processing Because of that GPUs are the preferred processor type to train a Deep Learning model Define latency and network bandwidth performance requirements for your models ーSome of your ML applications might require near instantaneous inference results to satisfy your business requirements Offering the lowest latency possible may require the removal of costly round trips to the nearest API endpoints This reduction in latency can be achieved by running the inference directly on the device itself This is known as Machine Learning at the Edge A common use case for such a requirement is predictive maintenance in factories This form of low latency and near real time inference at the edge allows for early indications of failure potentially mitigating costly repairs of machinery before the failure actually happens Continuously monitor and measure system performance ーThe practice of identifying and regularly collecting key metrics related to the building training hosting and running predictions against a model ensures that you are able to continuously monitor the holistic success across key evaluation criteria To validate system level resources used to support the phases of ML workloads it s key to continuously collect and monitor system level resources such as compute memory and network Requirements for ML workloads change in different phases as training jobs are more memory intensive while inference jobs are more compute intensive Durability and availabilityThere are key principles designed to help increase availability and durability specifically for ML workloads Manage changes to model inputs through automation ーML workloads have additional requirements to manage changes to the data that is used to train a model to be able to recreate the exact version of a model in the event of failure or human error Managing versions and changes through automation provides for a reliable and consistent recovery method Train once and deploy across environments ーWhen deploying the same version of an ML model across multiple accounts or environments the same practice of build once that is applied to application code should be applied for model training A specific version of a model should only be trained once and the output model artifacts should be utilized to deploy across multiple environments to avoid bringing in any unexpected changes to the model across environments Scalability and elasticityThere are key principles designed to help increase availability and durability specifically for ML workloads Identify the end to end architecture and operational model early ーEarly in the ML development lifecycle identify the end to end architecture and operational model for model training and hosting This allows for early identification of architectural and operational considerations that will be required for the development deployment management and integration of ML workloads Version machine learning inputs and artifacts ーVersioned inputs and artifacts enable you to recreate artifacts for previous versions of your ML workload Version inputs are used to create models including training data and training source code and model artifacts Automate machine learning deployment pipelines ーMinimize human touch points in ML deployment pipelines to ensure that ML models are consistently and repeatedly deployed using a pipeline that defines how models move from development to production Identify and implement a deployment strategy that satisfies the requirements of your use case and business problem If required include human quality gates in your pipeline to have humans evaluate if a model is ready to deploy to a target environment InterfacesCreating a data source is as simple as adding your data to S To ingest data you can use AWS Direct Connect to privately connect your data center directly to an AWS Region To physically transfer petabytes of data in batches use AWS Snowball or if you have exabytes of data use AWS Snowmobile You can integrate your existing on premises storage using Storage Gateway or add cloud capabilities using AWS Snowball Edge Use Amazon Kinesis Data Firehose to collect and ingest multiple streaming data sources Anti patternsAmazon ML has the following anti patterns Big data processing Data processing activities are well suited for tools like Apache Spark which provide SQL support for data discovery among other useful utilities On AWS Amazon EMR facilitates the management of Spark clusters and enables capabilities like elastic scaling while minimizing costs through Spot Instance pricing Real time analytics Collecting processing and analyzing the streaming data to respond in real time are well suited for tools like Kafka On AWS Amazon Kinesis makes it easy to collect process and analyze real time streaming data so you can get timely insights and react quickly to new information and Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data Apache Kafka is an open source platform for building real time streaming data pipelines and applications Hope this guide gives you an Introduction to Amazon Machine Learning Let me know your thoughts in the comment section And if you haven t yet make sure to follow me on below handles connect with me on LinkedInconnect with me on Twitter‍follow me on github️Do Checkout my blogs Like share and follow me for more content ltag user id follow action button background color important color fac important border color important Adit ModiFollow Cloud Engineer AWS Community Builder x AWS Certified x Azure Certified Author of Cloud Tech DailyDevOps amp BigDataJournal DEV moderator Reference Notes	2022-02-12 01:37:46
海外科学	NYT > Science	F.D.A Delays Review of Pfizer’s Covid Vaccine for Children Under 5	https://www.nytimes.com/2022/02/11/us/politics/fda-children-pfizer-vaccine.html	F D A Delays Review of Pfizer s Covid Vaccine for Children Under The agency will wait for data on whether three doses of Pfizer BioNTech s Covid vaccine are effective in young children after new disappointing data	2022-02-12 01:32:01
海外ニュース	Japan Times latest articles	U.S. vows stepped-up Indo-Pacific effort in pushback against China	https://www.japantimes.co.jp/news/2022/02/12/asia-pacific/us-indopacific-strategy-document-china/	U S vows stepped up Indo Pacific effort in pushback against ChinaThe document said the U S would focus on every corner of the region from South Asia to the Pacific Islands to strengthen its long term position	2022-02-12 10:32:27
海外ニュース	Japan Times latest articles	Japan set to gather foreign worker data for better support	https://www.japantimes.co.jp/news/2022/02/12/business/foreign-workers-japan-survey/	workers	2022-02-12 10:19:00
海外ニュース	Japan Times latest articles	Unpacking the marvelous harmony of Japanese tableware	https://www.japantimes.co.jp/life/2022/02/12/style/musubi-kiln/	harmony	2022-02-12 10:10:41
ニュース	BBC News - Home	'My prosthetic ears make me feel more normal'	https://www.bbc.co.uk/news/uk-england-leeds-60317898?at_medium=RSS&at_campaign=KARANGA	craven	2022-02-12 01:33:28
北海道	北海道新聞	トシリズマブをコロナ治療薬認定　ＷＨＯ、途上国普及見込む	https://www.hokkaido-np.co.jp/article/644873/	関節リウマチ	2022-02-12 10:19:00
北海道	北海道新聞	【道スポ】コンサＤＦ田中駿　急速仕上げだ　１２日熊本と最後の練習試合	https://www.hokkaido-np.co.jp/article/644870/	北海道コンサドーレ札幌	2022-02-12 10:17:00
北海道	北海道新聞	海洋保護、各国で取り組み急ぐ　仏西部で国際会合	https://www.hokkaido-np.co.jp/article/644864/	西部	2022-02-12 10:06:00
北海道	北海道新聞	ベルギーでコロナ規制緩和　新規感染者が減少傾向	https://www.hokkaido-np.co.jp/article/644863/	新型コロナウイルス	2022-02-12 10:04:00