投稿時間:2021-04-28 07:19:57 RSSフィード2021-04-28 07:00 分まとめ(25件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
IT	気になる、記になる…	米Microsoft、開発者向けカンファレンス｢Build 2021｣の参加登録の受付を開始	https://taisy0.com/2021/04/28/139788.html	build	2021-04-27 21:48:13
IT	ITmedia 総合記事一覧	[ITmedia News] Googleの親会社Alphabet決算、過去最高を更新　YouTube広告が好調	https://www.itmedia.co.jp/news/articles/2104/28/news063.html	alphabet	2021-04-28 06:32:00
Google	カグア！Google Analytics 活用塾:事例や使い方	KALDI スパイシービーフカレーは具沢山でスパイシー	https://www.kagua.biz/family/curry/20210428.html	kaldi	2021-04-27 21:00:50
AWS	AWS Game Tech Blog	Hyper-scale online games with a hybrid AWS Solution	https://aws.amazon.com/blogs/gametech/hyper-scale-online-games-with-a-hybrid-aws-solution/	Hyper scale online games with a hybrid AWS SolutionOnline multiplayer games such as multiplayer online battle arenas MOBA are becoming increasingly popular One option for game server hosting is to use on premises data centers which require multi year contracts for a set number of resources As the number of players for a given game grows developers have to determine what to do if they …	2021-04-27 21:41:17
AWS	AWS Machine Learning Blog	Announcing the AWS DeepComposer Chartbusters challenges 2021 season launch	https://aws.amazon.com/blogs/machine-learning/announcing-the-aws-deepcomposer-chartbusters-challenges-2021-season-launch/	Announcing the AWS DeepComposer Chartbusters challenges season launchWe re back with two new challenges for the AWS DeepComposer Chartbusters season Chartbusters is a global challenge in which developers use AWS DeepComposer to create original compositions and compete in monthly challenges to showcase their machine learning ML and generative artificial intelligence AI skills Regardless of your background in music or ML one of …	2021-04-27 21:22:12
AWS	AWS Media Blog	Sky: Something new at the royal wedding	https://aws.amazon.com/blogs/media/sky-something-new-at-the-royal-wedding/	Sky Something new at the royal weddingThis article originally appeared in FEED Magazine Issue Audiences love a love story especially one that ends in a wedding A year ago in May one young couple s nuptials became the UK s biggest media event The excitement around the wedding of Prince Harry and Meghan Markle extended well beyond the UK almost …	2021-04-27 21:28:37
AWS	AWS Security Blog	Hands-on walkthrough of the AWS Network Firewall flexible rules engine	https://aws.amazon.com/blogs/security/hands-on-walkthrough-of-the-aws-network-firewall-flexible-rules-engine/	Hands on walkthrough of the AWS Network Firewall flexible rules engineAWS Network Firewall is a managed service that makes it easy to provide fine grained network protections for all of your Amazon Virtual Private Clouds Amazon VPCs to ensure that your traffic is inspected monitored and logged The firewall scales automatically with your network traffic and offers built in redundancies designed to provide high availability AWS Network …	2021-04-27 21:21:29
AWS	AWS Podcast	#439: [INTRODUCING] Amazon FSx File Gateway	https://aws.amazon.com/podcasts/aws-podcast/#439	INTRODUCING Amazon FSx File GatewayLooking for fast efficient low latency access to cloud storage Nicki speaks with Laz Vekiarides Principal Product Manager AWS Storage Gateway File Gateway and Dave Stauffacher AWS Community Hero about how customers can optimize their on premises file based workloads using Amazon FSx File Gateway the newest gateway type in the AWS Storage Gateway portfolio Learn more about AWS Storage Gateway Explore Amazon FSx File Gateway Connect with an AWS Community Hero	2021-04-27 21:03:03
AWS	AWS	How to automate medical diagnosis using Amazon SageMaker	https://www.youtube.com/watch?v=Bx8r7GD1EKY	How to automate medical diagnosis using Amazon SageMakerAmazon SageMaker helps data scientists and developers to prepare build train and deploy high quality machine learning ML models quickly by bringing together a broad set of capabilities purpose built for ML In this demo video you ll learn how you can automate medical diagnosis using Amazon SageMaker To onboard quickly to SageMaker Studio visit Learn more about Amazon SageMaker Subscribe More AWS videos More AWS events videos AWS AmazonSageMaker MachineLearning	2021-04-27 21:41:49
AWS	AWS	How to deliver personalized recommendations using Amazon SageMaker	https://www.youtube.com/watch?v=o_p_8HXh0tY	How to deliver personalized recommendations using Amazon SageMakerAmazon SageMaker helps data scientists and developers to prepare build train and deploy high quality machine learning ML models quickly by bringing together a broad set of capabilities purpose built for ML In this demo video you ll learn how you can deliver personalized recommendations using Amazon SageMaker To follow along in the notebook visit and to onboard quickly to SageMaker Studio visit Learn more about Amazon SageMaker Subscribe More AWS videos More AWS events videos AWS AmazonSageMaker MachineLearning	2021-04-27 21:40:54
AWS	AWS Security Blog	Hands-on walkthrough of the AWS Network Firewall flexible rules engine	https://aws.amazon.com/blogs/security/hands-on-walkthrough-of-the-aws-network-firewall-flexible-rules-engine/	Hands on walkthrough of the AWS Network Firewall flexible rules engineAWS Network Firewall is a managed service that makes it easy to provide fine grained network protections for all of your Amazon Virtual Private Clouds Amazon VPCs to ensure that your traffic is inspected monitored and logged The firewall scales automatically with your network traffic and offers built in redundancies designed to provide high availability AWS Network …	2021-04-27 21:21:29
Linux	CentOSタグが付けられた新着投稿 - Qiita	Hyper-VにCentOS８を構築	https://qiita.com/taku-taku/items/d46b2987ac03cdccd2c2	後からでも変更可能です仮想マシンに認識させる仮想ハードディスクの名前や保存場所、サイズを指定して次へをクリックします。	2021-04-28 06:34:39
海外TECH	DEV Community	Sharpen your Ruby: Mastering Methods	https://dev.to/ericchapman/sharpen-your-ruby-mastering-methods-4l42	Sharpen your Ruby Mastering MethodsI develop in Javascript Python PHP and Ruby By far Ruby is my favorite programming language Together let start a journey and revisit our Ruby foundations Each post will include some theory but also exercise and solution If you have any questions comments or your are new and need help you can comment below or send me a message Whats is a Method Methods are a powerful feature for building Ruby programs they allow you to encapsulate behavior and call the method later on to build a full programs Method syntaxMethod name must start with a letter It may contain letters numbers an underscore or low line The convention is to use underscores to separate words in a multiword method nameMethod is declare with the def keyword followed by the method name and parameters and finish with a end keywordMethod parameters are specified after method name and are enclose in parentheses To invoke call the method you just use is nameExample def display message message puts messageend Calling the methoddisplay message Hello World or with optional parenthesesdisplay message Hello World Methods Return valueRuby specifically has a unique way with working with returned values Ruby automatically return the last line of the methoddef addition a b a bendputs addition That is the exact same thing as thisdef addition a b return a bendputs addition Since the last line is always return the return keyword is optional Attention This can be confusing def addition a b puts a bendputs addition emptySince the last line is always return Ruby return the results of the puts method and thats nothing So there is a clear difference between returning a b vs returning puts a bBy convention the keyword return is never use if we want to return the last line since that s the Ruby default But the keyword return need to be use if we want to return something before the last line def check a b if a gt return Number too high end Number are correct end call the method to test the resultcheck Number too highcheck Number are correctThis method will return Number too high if variable a is greater than After the return the method will end So the last line will never be executed If variable a is less or equal than The method will return Number are correct And agin since it is the last line of the method the return keyword is optional Method name that end with a In Ruby some method name end with a number number even truenumber odd falseBy convention methods that end with a always return a boolean value true or false You can create you own boolean method def is valid password if password length gt return true end falseend call the methodputs is valid secret trueMethod name that end with a In Ruby some method name end with a Those methods are call bang methods Bang method modify an object in place This can be dangerous because it change the object value and that may be not your intent For example Ruby have two reverse method one regular and one bang name reserve andname reverse The bang method will change the value of the object in placename Mike puts name reverse ekiM that method bang will have also update the name variableputs name ekiMMethods arguments default valueIt is possible to set default value for method parameterdef addition a b a bendaddition Since b is not specified Ruby use it default value of Methods Named ArgumentsSince a image is worth a thousand words let look at this example def calculation price shipping taxes price shipping fee taxesendcalculation As you can see with multiple arguments it can become difficult to read understand which arguments is what Named arguments is made for that kind of situation def calculation price shipping taxes price shipping fee taxesendcalculation price shipping taxes Now the method usage is clearer Another good thing about named arguments is that you can change the order of the arguments calculation taxes shipping price ExerciseCreate a little program that Create a method name subtraction with argumentsThat method with return the result of subtraction of the numbers pass as arguments If the last argument is not specified it will be treated as default value of Call that method and print its resultSolutiondef subtraction a b c a b cendputs subtraction ConclusionThat s it for today The journey just started stay tune for the next post very soon later today or tomorrow If you have any comments or questions please do so here or send me a message on twitter I am new on twitter so if you want to make me happyFollow me Follow justericchapman	2021-04-27 21:39:25
Apple	AppleInsider - Frontpage News	Apple products must come with three-year warranty in Spain	https://appleinsider.com/articles/21/04/27/apple-products-must-come-with-three-year-warranty-in-spain?utm_medium=rss	Apple products must come with three year warranty in SpainApple will be required to offer at least three years of warranty in Spain after the country approved a new national consumer protection standard Credit AppleThe Spanish Council of Ministers recently approved extending the existing warranty period to a mandatory three years Spain based blog iPadizate has reported According to Spain s Ministry of Consumer Affairs the move is meant to take the country a step further toward a circular economy Read more	2021-04-27 21:54:36
Apple	AppleInsider - Frontpage News	How to unlock iPhone with Apple Watch in iOS 14.5	https://appleinsider.com/articles/21/04/27/how-to-unlock-iphone-with-apple-watch-in-ios-145?utm_medium=rss	How to unlock iPhone with Apple Watch in iOS Apple in iOS and watchOS introduced a new feature that lets users more easily unlock their iPhones while wearing a mask Here s how it works Credit AppleInsiderAs Apple explains in a new support document the iPhone mask unlock feature lets a user unlock their iPhone if they re wearing an authenticated and paired Apple Watch without the use of Face ID The goal is to make it easier to unlock an iPhone when wearing a mask Read more	2021-04-27 21:10:05
海外科学	NYT > Science	An Artist Sketches the Giant Gender Gap on the Moon	https://www.nytimes.com/2021/04/27/science/moon-craters-women.html	contributions	2021-04-27 21:51:47
ニュース	BBC News - Home	Champions League semi-final: Karim Benzema earns Real Madrid draw against Chelsea	https://www.bbc.co.uk/sport/football/56880436	Champions League semi final Karim Benzema earns Real Madrid draw against ChelseaChelsea produce a composed performance to secure a draw and put themselves in a promising position after their Champions League semi final first leg against Real Madrid	2021-04-27 21:39:02
ニュース	BBC News - Home	World Snooker Championship: Anthony McGill leads Stuart Bingham 9-7, Kyren Wilson & Neil Robertson level at 8-8	https://www.bbc.co.uk/sport/snooker/56903358	World Snooker Championship Anthony McGill leads Stuart Bingham Kyren Wilson amp Neil Robertson level at Anthony McGill produces some sensational snooker to lead Stuart Bingham in their Crucible quarter final while Kyren Wilson draws level with Neil Roberston	2021-04-27 21:50:39
ビジネス	ダイヤモンド・オンライン - 新着記事	マイクロソフト1-3月期、市場予想上回る収益 - WSJ発	https://diamond.jp/articles/-/269906	市場予想	2021-04-28 06:13:00
LifeHuck	ライフハッカー［日本版］	コミュ障のための社会サバイバル術｜休めないときこそ情報遮断する「オフライン休日」を	https://www.lifehacker.jp/2021/04/233899book_to_read-750.html	遮断	2021-04-28 07:00:00
北海道	北海道新聞	米、屋外はマスク不要に　ワクチン条件、正常化前進	https://www.hokkaido-np.co.jp/article/538274/	疾病対策センター	2021-04-28 06:10:00
北海道	北海道新聞	五輪選手と定期接触者は毎日検査　食事場所や移動方法の制限も	https://www.hokkaido-np.co.jp/article/538273/	東京五輪	2021-04-28 06:10:00
北海道	北海道新聞	吉田、酒井、遠藤が候補　五輪サッカー男子のＯＡ枠	https://www.hokkaido-np.co.jp/article/538272/	日本代表	2021-04-28 06:10:09
GCP	Cloud Blog	How to transfer your data to Google Cloud	https://cloud.google.com/blog/topics/developers-practitioners/how-transfer-your-data-google-cloud/	How to transfer your data to Google CloudSo you ve decided to migrate your business to the cloudーgood call Now comes the question of transferring the data Here s what you need to know about transferring your data to Google Cloud and what tools are available Any number of factors can motivate your need to move data into Google Cloud including data center migration machine learning content storage and delivery and backup and archival requirements When moving data between locations it s important to think about reliability predictability scalability security and manageability Google Cloud provides four major transfer solutions that meet these requirements across a variety of use cases Click to enlarge Google Cloud Data Transfer OptionsYou can get your data into Google Cloud using any of four major approaches Cloud Storage transfer tools These tools help you upload data directly from your computer into Google Cloud Storage You would typically use this option for small transfers up to a few TBs These include the Google Cloud Console UI the JSON API and the GSUTIL command line interface  GSUTIL is an open source command line utility for scripted transfers from your shell It also enables you to manage GCS buckets  It can operate in rsync mode for incremental copies and streaming mode for pushing script output for large multi threaded multi processing data moves  Use it in place of the UNIX cp copy command which is not multithreaded Storage Transfer Service This service enables you to quickly import online data into Cloud Storage from other clouds from on premises sources or from one bucket to another within Google Cloud You can set up recurring transfer jobs to save time and resources and it can scale to s of Gbps To automate creation and management of transfer jobs you can use the storage transfer API or client libraries in the language of your choice As compared to GSUTIL Storage Transfer Service is a managed solution which handles retries and provides detailed transfer logging The data transfer is fast since the data moves over high bandwidth network pipes The on premise transfer service minimizes the transfer time by utilizing the maximum available bandwidth and by applying performance optimizations  Transfer Appliance This is a great option if you want to migrate a large dataset and don t have lots of bandwidth to spare Transfer Appliance enables seamless secure and speedy data transfer to Google Cloud For example a PB data transfer can be completed in just over days using the Transfer Appliance as compared the three years it would take to complete an online data transfer over a typical network Mbps Transfer Appliance is a physical box that comes in two form factors TA TB and TA TB The process is simple First you order the appliance through the Cloud Console  Once it is shipped to you you copy your data to the appliance via a file copy over NFS where the data is encrypted and secured Finally you ship the appliance back to Google for data transfer into your GCS bucket and the data is erased from the appliance Transfer appliance is highly performant because it uses all solid state drives minimal software and multiple network connectivity options BigQuery Data Transfer Service With this option your analytics team can lay the foundation for a BigQuery data warehouse without writing a single line of code It automates data movement into BigQuery on a scheduled managed basis It supports several third party sources along with transfers from Google SaaS apps external cloud storage providers and data warehouses such as Teradata and Amazon Redshift Once that data is in you can use it right inside BigQuery for analytics machine learning or just warehousing  ConclusionWhatever your use case for data transfer may be getting it done fast reliably securely and consistently is important And no matter how much data you have to move where it s located or how much bandwidth you have there is an option that can work for you For a more in depth look check out the documentation For more GCPSketchnote follow the GitHub repo For similar cloud content follow me on twitter pvergadia and keep an eye out on thecloudgirl devRelated Article cheat sheets to help you get started on your Google Cloud journeyWhether you need to determine the best way to move to the cloud or decide on the best storage option we ve built a number of cheat shee Read Article	2021-04-27 21:30:00
GCP	Cloud Blog	How capital markets can prepare for the future with AI	https://cloud.google.com/blog/topics/financial-services/how-capital-markets-can-prepare-for-the-future-with-ai/	How capital markets can prepare for the future with AIEditor s note This post originally appeared on Forbes BrandVoice In capital markets the stakes have been raised for participants to establish value win loyalty and expand their share of wallet An organization s data analytics capabilities combined with artificial intelligence and machine learning can open new opportunities in these areas But many organizations are still using data strategies from the past which limits their ability to harness data to its full potential and make the right business decisions Without the ability to accurately predict business outcomes with the help of AI market makers are left to rely on hunches and educated decision making when predicting the unknown Firms are increasingly recognizing the benefits of technology and partnering with modern tech providers is key to realizing those benefits But challenges still exist for firms looking to deploy ML at scale Below we ll look at some of those challenges along with tools and best practices that can help capital markets firms adopt and benefit from AI and ML strategies  Challenges in the data to ML journeyAt a high level the challenges faced in capital markets when performing AI are similar to other industries The first set of challenges comes with the data itself Unstructured data accounts for of enterprise data and many enterprises face the limitations of on premises and legacy applications that don t work well with newer cloud based tools Also a high number of data silos spread across capital markets are common due to growth through acquisitionsーa time consuming distraction that limits efficiency and decision making Data science is not hamstrung by the velocity of messages nor the volume but by the huge variety of disparate data sources Other challenges include the views and varying levels of resistance regarding the value of data by various stakeholders within the enterprise the restrictions of regulatory environments and the limited cloud skills of an enterprise s IT teams ML operations can also be challenging as firms enter this emerging technology area Adopting and benefiting from AI and ML strategies Tools and best practices Before you perfect AI get good at analyticsEffective AI and ML depend upon a strong and flexible data analytics platform which first may need some rearchitecting of its infrastructure Without a strong core data infrastructure it s hard to perform data science in production With enterprises that have adopted traditional data analytics platforms that live on local servers challenges aboundーand the blue dollar costs those charged back within the company go far beyond software licensing These enterprises have to expend costs and resources on monitoring performance tuning upgrading resource provisioning and scalability Business critical data sources may not be easily accessible by data scientists blocking business critical decision making All of these obstacles leave less time and room for gleaning analysis and insights from the data With a serverless cloud based data analytics model the vast majority of infrastructure maintenance and patching is handled by the cloud provider This enables your data team to devote more time and resources to analysis and insights Highly performant and integrated cloud technologies can help enterprises overcome data silos establish a single code base and contribute to a more collaborative workplace culture They can also be designed to provide more real time insightsーan invaluable building block of ML and AI In short effective core data infrastructure is a competitive advantage over other organizations that remain stuck in silos and servers   Get started by prioritizing a business goalIn the past several years alone a number of common use cases for AI have arisen in the capital markets sector Here are some specific examples and how AI can help Dynamically learn how best to place orders across venues with algorithmic execution Recognize potential triggers for unscheduled events with predictive data analytics to forecast events Generate multi dimensional risk and exposure data analytics with real time risk analysis Use ML to help gain insight into the selection process via algorithms for asset selection  Determine client needs opportunities using social media sentiment analysis  Build systems that can respond to client inquiries via speech to text natural language processing  Extract key data from unstructured or semistructured documents with natural language document analysis services  Generate performance and financial data commentary reporting with natural language generation for document writing  Identify complex trading patterns in large datasets with market abuse and financial crime surveillance  Though it s tempting to focus exclusively on the benefits that tech can bring to data analytics the immediate opportunity for how enterprises can fully benefit from AI rests in how humans and AI can work together ML based data analytics is more powerful when paired with human judgment and intuition Recent advancements in tech have made computers faster data storage cheaper and access to algorithms more democratized But human experience and judgment can contribute to and expand upon accurate insightful data analysis whether that be in medicine or in financial markets Model explainability and fairness are concrete examples of where human experience is critical to successful AI more on that below When designing an AI system for use cases like the ones listed above don t divorce it from the benefits of human wisdom   Structure your team for better data decisionsFinding retrieving and preprocessing data can be the most time consuming part of building ML models Over of model building effort goes here This challenge is not unique to financial services but addressing this challenge is a necessary prerequisite for ML and affords a competitive advantage Structuring your organization and internal teams to tackle this challenge will increase your odds for success but will require some planning and careful thought  Simply put the purpose of a data science team is to facilitate better decision making using data Keep this in mind when deciding how to best structure your data science and AI ML teams as well as who they ll be reporting to It s also important to consider where your organization currently sits in its data and AI journeys Consider culture size and the ways the company has grown Is your enterprise centralized or decentralized Is it federated Do you employ consultants When defining team roles consider how your flow of data is structured and where those roles would be of most efficient use Also don t limit yourselfーdifferent roles don t necessarily require different employees People can perform different roles as long as the roles are clearly defined   Understand the concepts of explainability and fairnessThere are two important considerations to keep in mind when structuring your organization for data analysis and AI The first is explainability We want AI systems to produce results as expected with transparent explanations and reasons for the decisions they make This is known as explainability a high priority here at Google as well as a growing area of concern for enterprises when it comes to designing their AI systems Explainability increases trust in the decisions of AI systems and a number of best practices have evolved to ensure that trust These include closely auditing your work and data science processes monitoring what s called “model drift also referred to as “concept drift including accuracy metrics and ensuring reproducibility of features  Fairness is another important topic in AI An algorithm is said to show fairness if its results are independent of certain variables especially those that may be considered sensitive These include individual traits that shouldn t correlate with the outcome like ethnicity gender sexual orientation or disability An accurate model may learn or even amplify problematic pre existing biases in the data based on those traits Identifying appropriate fairness criteria for a system requires accounting for UX cultural social historical political legal and ethical considerations several of which may have tradeoffs  Best practices for fairness include  Designing your model using concrete goals Monitoring goals through time for your system to work fairly across anticipated use casesーin a number of different languages for example or in a range of different age groups Using representative datasets to train and test your model Using a diverse set of testers Thinking about the model s performance across different sub groups Building your roadmap for the future with AI ML Capital markets rich history of using cutting edge technology now includes AI to open new opportunities in the sector Foresight and planning will ensure the best results from ML and AIーthey shouldn t be an afterthought for your organization That means building a strong core infrastructure for data analysis first planning the structure of internal teams that will use data and AI and using flexible cloud based tools to optimize results When introducing new AI ML strategies IT leaders must ensure that they integrate and fit with existing modernization efforts as opposed to being a bolt on afterthought This will lead to a true integration of AI ML and business Related ArticleFive habits of highly effective capital markets firms who run in the cloudLearn how capital markets firms are innovating in the cloud taking inspiration from technology companies and focusing on collaboration Read Article	2021-04-27 21:30:00