投稿時間:2021-07-31 06:17:05 RSSフィード2021-07-31 06:00 分まとめ(19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
TECH	Engadget Japanese	2020年7月31日、筆圧対応のデジタルノート「フリーノ」の一般販売が開始されました：今日は何の日？	https://japanese.engadget.com/today-203035551.html	一般販売	2021-07-30 20:30:35
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【wordpress】１つのサイトにポートフォリオサイトを複数表示させたい	https://teratail.com/questions/352068?rss=all	【wordpress】つのサイトにポートフォリオサイトを複数表示させたい現在、wordpressを利用してポートフォリオサイトを作成しています。	2021-07-31 05:45:49
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Node.jsでルーティングして割り当てられたアドレスの処理をsocket通信にしたい	https://teratail.com/questions/352067?rss=all	Nodejsでルーティングして割り当てられたアドレスの処理をsocket通信にしたい現在Nodejsを用いたチャットアプリの開発を行なっており、ルーティングして割り当てられたアドレスの処理をsocket通信にしたいと考えています。	2021-07-31 05:28:47
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	プラグインを使って作成したカスタム投稿タイプ間の親子関係	https://teratail.com/questions/352066?rss=all	tomnbsppostnbsptypenbspui	2021-07-31 05:07:24
海外TECH	DEV Community	What was your win this week?	https://dev.to/devteam/what-was-your-win-this-week-4129	What was your win this week Hey there Looking back on your week what was something you re proud of All wins count ーbig or small Examples of wins include Starting a new projectFixing a tricky bugScheduling some time off or whatever else might spark joy ️Wishing you more wins to come ーand as much relaxation as this cat is experiencing	2021-07-30 20:10:29
Apple	AppleInsider - Frontpage News	How to use Quick Notes in iPadOS 15	https://appleinsider.com/articles/21/07/30/how-to-use-quick-notes-in-ipados-15?utm_medium=rss	How to use Quick Notes in iPadOS It s not yet the most stable part of the iPadOS beta but Quick Notes on the iPad is already excellent ーand it s going to be great Quick Notes on the iPad is a boonQuick Notes is first class on the Mac where the speed of opening a new note to capture a thought is so fast that it s going to dent the sales of third party rivals Quick Notes should be even better on an iPad because it s still easier to use one app at a time on iPadOS yet we all need to jot things down through the day Read more	2021-07-30 20:47:50
海外科学	NYT > Science	Those Virus Sequences That Were Suddenly Deleted? They’re Back	https://www.nytimes.com/2021/07/30/science/coronavirus-sequences-lab-leak.html	Those Virus Sequences That Were Suddenly Deleted They re BackChinese researchers have uploaded genetic sequences of coronaviruses to a scientific database more than a year after they took them offline	2021-07-30 20:20:41
ニュース	BBC News - Home	Israel accuses Iran over deadly oil tanker attack	https://www.bbc.co.uk/news/world-middle-east-57977702	iranian	2021-07-30 20:26:48
ニュース	BBC News - Home	Stokes takes break from cricket for mental health	https://www.bbc.co.uk/sport/cricket/58033393	Stokes takes break from cricket for mental healthBen Stokes will take an indefinite break from all cricket with immediate effect and has withdrawn from England s Test squad for the series against India	2021-07-30 20:42:23
ニュース	BBC News - Home	Man City make £100m bid for Aston Villa's England midfielder Grealish	https://www.bbc.co.uk/sport/football/58032330	grealish	2021-07-30 20:45:25
ビジネス	ダイヤモンド・オンライン - 新着記事	富裕層で密かに流行「離婚対策術」、月100万円もざら？「婚姻費用地獄」回避法 - 海外の節税　富裕層の相続	https://diamond.jp/articles/-/276949	富裕層で密かに流行「離婚対策術」、月万円もざら「婚姻費用地獄」回避法海外の節税富裕層の相続米アマゾン・ドット・コムの創業者、ジェフ・ベゾス氏や米マイクロソフトの創業者、ビル・ゲイツ氏など、ビリオネアの立て続けの離婚が話題になっている。	2021-07-31 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「想定外の節税術」が相続法改正で爆誕！富裕層注目の裏で思わぬ落とし穴も - 海外の節税　富裕層の相続	https://diamond.jp/articles/-/276948	落とし穴	2021-07-31 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	富士フイルムHD、コロナ禍最高益の背景にあるフィルム事業「消滅危機」の経験 - 事例で身に付く 超・経営思考	https://diamond.jp/articles/-/277174		2021-07-31 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「家賃0円」乱発！都内オフィス空室率急上昇で貸し手窮地、借り手ウハウハ - 五輪後の不動産・マンション	https://diamond.jp/articles/-/277466	東京都内	2021-07-31 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	キリンの6月ビール販売数6％減とアサヒの販売額9％減に、「意外な大差」がある理由 - コロナで明暗！【月次版】業界天気図	https://diamond.jp/articles/-/278317	キリンの月ビール販売数減とアサヒの販売額減に、「意外な大差」がある理由コロナで明暗【月次版】業界天気図コロナ禍から企業が復活するのは一体、いつになるのだろうか。	2021-07-31 05:05:00
北海道	北海道新聞	緊急事態拡大　首相の楽観　危機招いた	https://www.hokkaido-np.co.jp/article/573215/	緊急事態	2021-07-31 05:05:00
ビジネス	東洋経済オンライン	ワールドがリスク覚悟で｢超格安店｣を出す事情 出店拡大に踏み切るが｢諸刃の剣｣のジレンマも | 専門店･ブランド･消費財 | 東洋経済オンライン	https://toyokeizai.net/articles/-/444638?utm_source=rss&utm_medium=http&utm_campaign=link_back	ampbridge	2021-07-31 06:00:00
ビジネス	東洋経済オンライン	｢米国株100％､日本株は不要｣という人の落とし穴 日本株には実は｢これから期待できる点｣がある | 新競馬好きエコノミストの市場深読み劇場 | 東洋経済オンライン	https://toyokeizai.net/articles/-/444594?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2021-07-31 05:30:00
GCP	Cloud Blog	Monitor models for training-serving skew with Vertex AI	https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-ai/	Monitor models for training serving skew with Vertex AIThis blog post focuses on how Vertex AI enables one of the core aspects of MLOps monitoring models deployed in production for training serving skew Vertex AI a managed platform that allows companies to accelerate the deployment and maintenance of artificial intelligence AI models Here we will describe how Vertex AI makes it easy to Turn on skew detection for a model deployed in Vertex AI s Online Prediction service No prior pre processing tasks are required Just run a command with a few basic parameters to turn on monitoring Get alerted when data skew is detected Visualize the skew in a console UI to quickly diagnose the issue and determine the appropriate corrective action Model Monitoring explained in one minute Cartoons by courtesy of Google Comics Factory What is training serving skew and how does it impact models deployed in productionHere is a definition of training serving skew from Rules of Machine Learning Best Practices for ML Engineering Training serving skew is a difference between model performance during training and performance during serving This skew can be caused by A discrepancy between how you handle data in the training and serving pipelines A change in the data between when you train and when you serve A feedback loop between your model and your algorithm In this blog post we will focus on tooling to help you identify the issues described by the first two bullets above any change in the data feature values between training and serving also known as data drift or covariate shift The feedback loop problem mentioned in the third bullet point has to be addressed by proper ML system design Please refer to this blog post for a description of how the Vertex Feature Store can help avoid this feedback loop problem Changes in the input data can occur for multiple reasons a bug inadvertently introduced to the production data pipeline a fundamental change in the concept the model is trained to predict a malicious attack on your service and so on Let s look at a few real world examples that impacted Google applications in the past This paper Data Validation for Machine Learning describes the following incident A ML pipeline trains a new ML model every dayAn engineer does some refactoring of the serving stack inadvertently introducing a bug that pins a specific feature to Because the ML model is robust to data changes it doesn t output any error and continues to generate predictions albeit with lower accuracyThe serving data is reused for training the next model Hence the problem persists and gets worse until it is discovered As this scenario illustrates training serving skew can sometimes be as harmful as a P bug in your program code To detect such issues faster Google introduced a rigorous practice of training serving data skew detection for all production ML applications As stated in this TFX paper Let s look at how this practice helped Google Play improve app install rate By comparing the statistics of serving logs and training data on the same day Google Play discovered a few features that were always missing from the logs but always present in training The results of an online A B experiment showed that removing this skew improved the app install rate on the main landing page of the app store by   from TFX A TensorFlow Based Production Scale Machine Learning Platform Thus one of the most important MLOps lessons Google has learned is continuously monitor model input data for changes For a production ML application this is just as important as writing unit tests Let s take a look at how skew detection works in Vertex AI  How is skew identifiedVertex AI enables skew detection for numerical and categorical features For each feature that is monitored first the statistical distribution of the feature s values in the training data is computed Let s call this the “baseline distribution The production i e serving feature inputs are logged and analyzed at a user determined time interval This time interval is set to hours by default and can be set to any value greater than hour For each time window the statistical distributions of each monitored feature s values are computed and compared against the aforementioned training baseline A statistical distance score is computed between the serving feature distribution and training baseline distribution JS divergence is used for numerical features and L infinity distance is used for categorical features When this distance score exceeds a user configurable threshold it is indicative of skew between the training and production feature values Measuring how much the data changedSetup monitoring by running one simple commandOur goal is to make it very easy to turn on monitoring for a model deployed on Vertex AI s Prediction service almost as easy as just flipping a switch Once a prediction endpoint is up and running one can turn on training serving skew detection by running a single gcloud command and soon via a few clicks in the UI no need for any pre processing or extra setup tasks To setup skew detection for a prediction endpoint simply run a gcloud command such as Let s look at some of the key parameters full gcloud docs are available here emails The email addresses to which you would like monitoring alerts to be sentendpoint the prediction endpoint ID to be monitoredprediction sampling rate For cost efficiency it is usually sufficient to monitor a subset of the production inputs to a model This parameter controls the fraction of the incoming prediction requests that are logged and analyzed for monitoring purposesdataset For calculating the baseline you can specify the training dataset via one of four options a BigQuery table a CSV file on Cloud Storage a TFRecord file on Cloud Storage or a managed dataset on Vertex AI Please review the gcloud docs for information about the parameters “bigquery uri “dataset “data format and “gcs uris  target field This specifies the field or column in the training dataset also sometimes referred to as the label that the model is trained to predict  monitoring frequency The time interval at which production i e serving inputs should be analyzed for skew This is an optional parameter It is set to hours by default feature thresholds Specify which input features to monitor along with the alerting threshold for each feature The alerting threshold is used to determine when an alert should be thrown This is an optional parameter By default a threshold of is used for each feature Get alerts and visualize data in the console UIWhen a skew is detected for a feature an alert is sent via email More ways of receiving alerts will be added in the near future including mechanisms to trigger a model retraining pipeline  Upon getting an alert users can log into the console UI to visualize and analyze the feature value distributions Users can perform side by side visualization of the production data distributions and training data distributions to diagnose the issue Next StepsNow Model Monitoring is available as Preview Anyone can try it from the Model Monitoring document and there is also a great instruction demo video and example notebook created by Marc Cohen that provides the end to end scenario from deploying a model to an Endpoint to monitor the model with Model Monitoring Take the first step into the real world MLOps with Google s best practices for productionizing ML systems Related ArticleKickstart your organization s ML application development flywheel with the Vertex Feature StoreA Feature Store is a key ingredient for MLOps helping accelerate development deployment and management of production ML applications Read Article	2021-07-30 20:30:00