投稿時間:2022-09-23 03:32:40 RSSフィード2022-09-23 03:00 分まとめ(35件)

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AWS	AWS Management Tools Blog	Integrating Kubecost with Amazon Managed Service for Prometheus	https://aws.amazon.com/blogs/mt/integrating-kubecost-with-amazon-managed-service-for-prometheus/	Integrating Kubecost with Amazon Managed Service for PrometheusThis blog post was co written by Linh Lam Solution Architect Kubecost Customers can track their Kubernetes control plane and Amazon Elastic Compute Cloud Amazon EC costs using AWS Cost and Usage Reports However they often need deeper insights to accurately track Kubernetes costs across namespaces clusters pods and more We recently announced that AWS and …	2022-09-22 17:39:11
Docker	dockerタグが付けられた新着投稿 - Qiita	【M1Mac】Docker ComposeでRails開発環境構築	https://qiita.com/rexid/items/4ea724cef462560039da	dockercompose	2022-09-23 02:02:30
Ruby	Railsタグが付けられた新着投稿 - Qiita	【M1Mac】Docker ComposeでRails開発環境構築	https://qiita.com/rexid/items/4ea724cef462560039da	dockercompose	2022-09-23 02:02:30
技術ブログ	Developers.IO	[新サービス]インシデントからの復旧を有人でサポートしてくれるAWS Incident Detection and Responseがリリースされました	https://dev.classmethod.jp/articles/aws-incident-detection-response-release/	awsenterpris	2022-09-22 17:42:09
海外TECH	Ars Technica	Is Moore’s law actually dead this time? Nvidia seems to think so	https://arstechnica.com/?p=1883729	moore	2022-09-22 17:08:51
海外TECH	DEV Community	Machine Learning Forecasting for Enhancing Business Intelligence	https://dev.to/anastasiiamolodoria/machine-learning-forecasting-for-enhancing-business-intelligence-2mhi	Machine Learning Forecasting for Enhancing Business IntelligenceBusiness forecasting is imperative for making balanced financial and operational decisions Its impact across industries has grown in recent years due to the way companies build data driven strategies and rely on data But let s find out what is needed for efficient forecasting and why machine learning models have all the prerequisites for enhancing business intelligence In this article we ll go over the principles of ML forecasting functioning and the benefits it can bring if used for business purposes Also we will highlight the differences between machine learning forecasting models from regression to exponential smoothing How AI Improves Business Forecast AccuracyThanks to forecasting companies are able to better serve clients and ship orders instead of running out of stock This leads to a huge impact on sales and customer satisfaction For example knowing the demand brings an ability to manage logistics and track inventory costs or even predict ROI for a new product Therefore ML forecasting models allow organizations to enhance their AI maturity and more importantly to solve business tasks by looking at existing data Nowadays the volume of data from markets industries and users is skyrocketing FinancesOnline reveals that the world will produce and consume zettabytes in Such growth fuels the training of ML models making them more robust and accurate According to Market Research Future the ML market share is projected to reach B by with a CAGR of during the forecast period of With increasing market share caused by evolving cloud based services and growth in unstructured data comes new opportunities for building forecasting models So let s figure out how these models improve business forecast accuracy and why they are more efficient than traditional approaches ML forecasting rests on an enormous amount of information which can be analyzed to achieve accurate predictions and high performance rates Unlike traditional forecasting approaches machine learning allows companies to consider numerous business drivers and factors and for building nonlinear algorithms to minimize loss functions a crucial ingredient in all optimization problems Training of any ML forecasting model requires the assessment stage This stage foresees comparison of predicted and actual results It brings an understanding of how well the model performs After that it would be possible to compare different forecasting algorithms and choose the one which produces a minimal amount of errors With this approach businesses can replace traditional techniques with ML getting the following benefits for their business forecast Acquiring insights and detecting hidden patterns that are difficult to trace with traditional approaches Training ML forecasting models on BigData and moving computation to Cloud is becoming de facto an industry standard Reduced number of errors in forecasting For instance McKinsey claims that AI driven forecasting models applied to delivery chain management can reduce the number of errors by Ability to infuse more data in a model External data may be valuable here and change the outcomes in terms of predictions Flexibility and rapid adaptability to changes Compared to traditional non AI approaches ML forecasting algorithms can be quickly adapted in case of any significant changes Please note that we re considering forecasting not predictive modeling We ll explain the difference between these two models in simple terms Difference Between Forecasting amp PredictiveModelingBoth forecasting and predictive algorithms are applied to address cumbersome challenges related to business planning customer behavior and decision making But nevertheless these techniques differ Forecasting modeling implies analysis of past and present data to find patterns or trends which allow us to estimate the probability of future events In contrast to predicting forecasting modeling should have traceable logics Typical use cases include a forecast for energy consumption in the following months an evaluation of how many customers will reach support in the next days or how many agreements for the supply are expected to be signed All this could be forecasted based on previous historical data Predictive modeling is the process of applying AI and data mining to assess more detailed specific outcomes and use much more diverse data types The difference between predictive and forecasting modeling is blurred still we can consider an example to understand it better Just imagine that a credit institution plans to launch a new premium card At this point two questions may arise The first will probably be how many cards will be issued in the next months Forecasting modeling will help us find an answer to this question thanks to analysis of similar products launched in the past But we still don t know whom we can recommend this card to Here predictive modeling comes into play It enables us to analyze a customer information database with such fields as age salary preferences consumer habits etc With this approach we will eventually understand which clients are more likely to use this card Use Сases For Machine Learning Forecasting For Business FINANCIAL FORECASTINGWithout a financial forecast companies face disruption in processes and performance while C level managers tend to make incorrect decisions That s why companies leverage ML forecasting which instead of dealing with mundane tasks concentrates attention on understanding business drivers Moreover ML financial forecasting reduces the amount of ineffective strategies in play and human errors and helps predict supply demand inventory future revenues expenses and cash flow For example stakeholders of the business are aiming to know the company s turnover and key factors for growth during the next financial period to understand and analyze areas of improvement Based on historical key company business indicators and existing turnover information during the past periods we can develop an ML forecasting model using deep learning or regression models It will predict future required metrics based also on seasonal information and other influencing factors In this case business owners will be able to plan the next period of time accordingly SUPPLY CHAIN FORECASTINGML can fully transform management in the area of supply chains which are becoming more globalized and sophisticated ML based forecasting solutions enable companies to efficiently respond to issues and threats as well as avoid under and overstocking Machine learning algorithms for forecasting can learn relationships from a training dataset and then apply these relationships to new data Thus ML improves selecting and segmenting suppliers predicting supply chain risks inventory management and transportation and distribution processes Let s look at an example of using machine learning for supply chain forecasting The chain of hypermarkets operates around stores in different locations and has an average of SKUs per store For such a big chain it s definitely required that the process of replenishment of warehouses be automated There are two main benefits in this case No need to store a lot of hard to sell productsFrequently sold products should be delivered on timeBased on the previous information on replenishment of warehouses as well as data that shows how fast certain products are selling we can develop an ML model for predicting the number of products per SKU The prediction could be shown with different time horizons e g daily weekly monthly etc This can help managers properly organize the system of storing products and minimize the case of product absence PRICE PREDICTIONPrice prediction algorithms determine how much the product must cost to be appealing to consumers meet the company s expectations and assure the highest level of sales The construction of price forecasts should take into account such factors as product features demand and existing trends This approach may be perceived skeptically yet it s beneficial when companies enter a new market or release a new product and want to easily cope with a myriad of fluctuating factors Often business owners want to have an understanding of price changes for a specific product for a future period of time Having taken into consideration client data with related price changes for a past period of time for all of the existing products we can catch general patterns from the previous data and extrapolate them for the next periods The positive impact could also be applied by adding external third party data that could influence prices as well for instance inflation rate holidays seasonal patterns etc Wrapping up all of this data we can develop an ML forecasting model that will be able to predict price trends for specific products DEMAND amp SALES FORECASTINGA fluctuation in demand is a cumbersome challenge that concerns the whole e commerce industry That s why companies including manufacturers apply ML demand forecasting to predict buyers behavior and find out how many products to produce or order With ML models it s possible to avoid excess inventory or stockout Moreover such an approach to demand forecasting enables understanding the target audience and competition Let s say a restaurant chain business wants to plan demand in advance It will help the business in several ways to know the number of dishes that will be sold in the restaurant in order to plan food stock in advance to understand and define an appropriate number of employees that are required to provide quality customer serviceto come up with the proper and timely marketing campaignIn order to develop a demand forecasting model and help businesses to fulfill their goals it will be great to start by analyzing historical data of the previous periods One of the ways to improve the model performance could be an integration of NLP algorithms as well For example we can consider reviews on Google for our restaurant chain as well as the main competitors to identify the main dishes quality of service that customers like or do not like FRAUD DETECTIONAccording to a TransUnion report there is a increase in the rate of suspected digital fraud globally between and It indicates that companies should make greater efforts in the development of anti fraud tactics ML algorithms can detect suspicious financial transactions by learning from past data They are already successfully applied in e commerce banking healthcare fintech and other areas For instance a cafe chain owner wants to analyze the productivity of employees One of the main goals is to detect hidden patterns that allow employees to cheat Different frauds like this could lead to losing money Based on historical data we can develop a fraud detection model that will detect anomaly patterns and notify about them In this case managers can precisely analyze detected anomalies and identify the root cause of such deviations in the data In the future such cases could be prevented by the manager to keep the business safe Key Machine Learning Forecasting AlgorithmsLet s look at some key machine learning forecasting algorithms to better understand how ML forecasting can be applied REGRESSION ALGORITHMSML regression models are applied to predict trends and outcomes being capable of comprehending how variables impact each other along with the results The dependency between variables can be both linear and nonlinear while labeled data is required for training After understanding the relationship of variables regression models can predict what results will be in unseen data Simple and multiple linear regression and logistic regression where a target variable has only two values are one of the most common baseline models to predict sales stock prices and customer behavior DEEP LEARNING ALGORITHMSTime series forecasting implementation is gradually replenishing with new deep learning algorithms The more versatile and explainable a model is the higher the chances for its production use Let s take a look at a few deep learning models for time series forecasting The first one is DeepAR It s a supervised ML algorithm created by Amazon and based on recurrent neural networks It has proven its efficiency with datasets consisting of hundreds of interrelated time series The advantages of the method are the possibility to use a rich set of inputs scaling capabilities and suitability for probabilistic forecasting The second one is the Temporal Fusion Transformer TFT It overcomes other deep learning models in terms of versatility and can be built on multiple time series TFT performs well even if trained on a small dataset thus being suitable for demand forecasting as just one example The third algorithm is long short term memory LSTM based upon an artificial RNN in which the output from one step is transformed into the input of the next step As for the architecture of LSTM it consists of neural networks and memory cells for maintaining data while any manipulation within the memory is performed by gates There are three gates here Forget Input and Output However LSTM requires plenty of resources and a long time for training TREE BASED ALGORITHMSTree based algorithms refer to supervised learning approaches Their advantages include accuracy sustainability and suitability for mapping non linear patterns The idea here is to define homogeneous sets in the sample taking into account the key differentiator in input The classification of tree based algorithms depends on the target variable As for advantages tree based algorithms can be easily grasped require minimal data cleaning and handle different types of variables The tendency toward overfitting and irreconcilability with continuous variables may be seen as disadvantages in this case GAUSSIAN PROCESSESGaussian processes GP are inferior in popularity to other models yet they are powerful enough for industrial application including automatic forecasting Gaussian processes enable us to incorporate expert opinion via kernel though their application in forecasting depends on the number of parameters and may be expensive AUTO REGRESSIVE ALGORITHMSThe group of auto regression algorithms foresees predicting future values using the output from the previous step as an input Forecasting algorithms of this group include ARIMA SARIMA and others In ARIMA forecasting is carried out with the application of moving and autoregressive averages For instance the ARIMA model can predict fuel costs or forecast a company s revenue based on past periods SARIMA uses the same basic idea but it includes a seasonal component that may affect the outcomes EXPONENTIAL SMOOTHINGExponential smoothing is an alternative to ARIMA models It can be applied as a forecasting model for univariate data that can be extended to support data with a systematic trend or seasonal component In this model forecasting is a weighted sum of past observations yet the importance weight of past observations is exponentially decreased The accuracy of prediction depends on the type of the exponential smoothing model which can be single double or triple The most sophisticated exponential smoothing models take into account trends and seasonality How to Apply Machine Learning ForecastingRegardless of the chosen model the whole adoption of ML practices looks as the following Define business goals and available internal dataSearch for external data namely market reports trends GDPs product reviews etc Structure clean and label data if needed Identify the batch of problems to be solved with the help of forecastingSelect a baseline model usually simple regression or tree based models to be used as a first reference point to start with Improve models performance by implementing more sophisticated ML models or adjusting the dataAfter achieving comfortable results the model is implemented into production added to existing software and used on more data Challenges of ML ForecastingNothing good comes without challenges ML forecasting is no exception Key business forecasting with machine learning challenges include the following Insufficient amount of data to train a modelAn incorrectly chosen metric to evaluate results in alignment with business needsImputation of missing dataDealing with outliers anomaliesWhile infusing the data at the scale of AI businesses encounter difficulties and limitations that s why it s crucial to involve experienced data science professionals and AI engineers when implementing machine learning	2022-09-22 17:31:52
Apple	AppleInsider - Frontpage News	Nomad releases Ultra Orange Apple Watch band & iPhone 14 Pro case	https://appleinsider.com/articles/22/09/22/nomad-releases-ultra-orange-apple-watch-band-iphone-14-pro-case?utm_medium=rss	Nomad releases Ultra Orange Apple Watch band amp iPhone Pro caseNomad has released Ultra Orange a limited edition collection that includes an Apple Watch sport band and a rugged case for the iPhone Pro models Nomad Ultra Orange collectionThe Apple Watch Ultra starts shipping to customers on Friday September and Nomad s Sport Band may be the perfect companion It and the iPhone Pro case of these products are available in a few colors ーonly the Ultra Orange variant is limited edition Read more	2022-09-22 17:45:57
Apple	AppleInsider - Frontpage News	Apple Watch Ultra owners will need to upgrade to watchOS 9.0.1	https://appleinsider.com/articles/22/09/22/apple-watch-ultra-owners-will-need-to-upgrade-to-watchos-901?utm_medium=rss	Apple Watch Ultra owners will need to upgrade to watchOS Apple has released watchOS for Apple Watch Ultra to address bugs and performance issues that may affect day one adopters Update Apple Watch Ultra to watchOS The Apple Watch Ultra arrives in customers hands on Friday September and it will need an immediate update It isn t clear what the update addresses but if users want to avoid service issues or other setup problems it should be the first thing they do Read more	2022-09-22 17:42:05
Apple	AppleInsider - Frontpage News	DJI releases Osmo Mobile 6 for video stabilization on smartphones	https://appleinsider.com/articles/22/09/22/dji-releases-osmo-mobile-6-for-video-stabilization-on-smartphones?utm_medium=rss	DJI releases Osmo Mobile for video stabilization on smartphonesOn Thursday DJI announced DJI Osmo Mobile a handheld stabilizer that works for smartphones and works in tandem with the iPhone s Action Mode DJI Osmo Mobile The iPhone lineup received Action Mode a software feature that stabilizes videos but sometimes hardware such as the Osmo Mobile is better for specific uses Osmo Mobile features axis stabilization a new Quick Launch feature and ActiveTrack Read more	2022-09-22 17:13:28
Apple	AppleInsider - Frontpage News	Here's where to save on Apple's brand-new AirPods Pro 2	https://appleinsider.com/articles/22/09/21/heres-where-to-save-on-apples-brand-new-airpods-pro-2?utm_medium=rss	Here x s where to save on Apple x s brand new AirPods Pro Apple AirPods Pro are already discounted with the lowest prices at your fingertips in our AirPods Price Guide Apple AirPods are already on sale despite officially launching on September rd The newest member of the AirPods product line AirPods feature Apple s H chip along with touch control to let you adjust volume answer and end calls or switch between Active Noise Cancellation and Adaptive Transparency Read more	2022-09-22 17:30:20
海外TECH	CodeProject Latest Articles	Rethinking the Web (RTW) - Web Inversion	https://www.codeproject.com/Articles/5342271/Rethinking-the-Web-RTW-Web-Inversion	worker	2022-09-22 17:01:00
金融	金融庁ホームページ	インターネットバンキングによる預金の不正送金事案が多発しています。	https://www.fsa.go.jp/ordinary/internet-bank_2.html	不正送金	2022-09-22 18:00:00
金融	金融庁ホームページ	OECDコーポレートガバナンス委員会による市中協議文書「G20/OECDコーポレートガバナンス原則の見直し」について掲載しました。	https://www.fsa.go.jp/inter/etc/20220922/20220922.html	goecd	2022-09-22 17:45:00
金融	金融庁ホームページ	企業会計審議会総会・第９回会計部会の開催について公表しました。	https://www.fsa.go.jp/news/r4/singi/20220922.html	企業会計	2022-09-22 17:04:00
金融	金融庁ホームページ	企業会計審議会委員の任命について公表しました。	https://www.fsa.go.jp/news/r4/singi/20220922-2.html	企業会計	2022-09-22 17:03:00
ニュース	BBC News - Home	Khayri Mclean: Huddersfield stabbing victim named as boy arrested	https://www.bbc.co.uk/news/uk-england-leeds-62992837?at_medium=RSS&at_campaign=KARANGA	khayri	2022-09-22 17:54:10
ニュース	BBC News - Home	Ukraine war: Mum of Aiden Aslin thought son's release 'would never happen'	https://www.bbc.co.uk/news/uk-62998902?at_medium=RSS&at_campaign=KARANGA	aiden	2022-09-22 17:11:35
ニュース	BBC News - Home	Rail strikes: New date set for 40,000 workers to walk out	https://www.bbc.co.uk/news/business-62999136?at_medium=RSS&at_campaign=KARANGA	services	2022-09-22 17:01:16
ニュース	BBC News - Home	Tesla ordered to recall more than a million US cars	https://www.bbc.co.uk/news/technology-62996103?at_medium=RSS&at_campaign=KARANGA	tesla	2022-09-22 17:05:15
ニュース	BBC News - Home	ECB proposals equally unworkable as current schedule in county cricket, says Sussex chief	https://www.bbc.co.uk/sport/cricket/62999460?at_medium=RSS&at_campaign=KARANGA	ECB proposals equally unworkable as current schedule in county cricket says Sussex chiefThe chair of Sussex County Cricket Club Jon Filby says the English game s current schedule is unworkable but proposed changes are equally unworkable	2022-09-22 17:52:38
ニュース	BBC News - Home	County Championship: Surrey beat Yorkshire by 10 wickets to win title	https://www.bbc.co.uk/sport/cricket/62988999?at_medium=RSS&at_campaign=KARANGA	championship	2022-09-22 17:05:29
ニュース	BBC News - Home	Football disorder in England and Wales reaches eight-year high - Home Office	https://www.bbc.co.uk/sport/football/62989792?at_medium=RSS&at_campaign=KARANGA	Football disorder in England and Wales reaches eight year high Home OfficeArrests and reported incidents of disorder at football matches in England and Wales last season were at their highest level for eight years	2022-09-22 17:12:34
ニュース	BBC News - Home	National Insurance: Will tax cut save me money?	https://www.bbc.co.uk/news/uk-politics-58436009?at_medium=RSS&at_campaign=KARANGA	insurance	2022-09-22 17:35:47
ビジネス	ダイヤモンド・オンライン - 新着記事	【働き方ミニマリストが教える】 「心を軽くする」モノ選び8つのルール - 超ミニマル主義	https://diamond.jp/articles/-/310099	【働き方ミニマリストが教える】「心を軽くする」モノ選びつのルール超ミニマル主義「手放し、効率化し、超集中」するための全技法とはベストセラー『自由であり続けるために代で捨てるべきのこと』以来、四角大輔が年ぶりに書き下ろしたビジネス書『超ミニマル主義』の中から、「サイフ」「カバン」「書類」「名刺」「ウェア」「シューズ」「仕事机」「デバイス」「部屋」といった物質、「情報」「データ」「スケジュール」「タスク」「労働時間」「ストレス」「人付き合い」といった非物質を、極限まで「最小・最軽量化」する方法を紹介していく。	2022-09-23 02:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「褒め上手な人」はどこを褒める？知っておきたい褒め方の基本 - おもろい話し方	https://diamond.jp/articles/-/309640	話し方	2022-09-23 02:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【マンガ】「ブタの貯金箱」が教えてくれた幸せになるための秘訣 - サイコロジー・オブ・マネー	https://diamond.jp/articles/-/310073	貯金箱	2022-09-23 02:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【「ひとり反省会」のワナ】あなたは全然悪くない！疲れるだけの思い込みが即効で消える、今すぐできる小さな方法 - とても傷つきやすい人が無神経な人に悩まされずに生きる方法	https://diamond.jp/articles/-/309717		2022-09-23 02:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【出口学長・日本人が最も苦手とする哲学と宗教特別講義】 9割の人が知らない！ 朱子を読み解くおすすめの一冊 - 哲学と宗教全史	https://diamond.jp/articles/-/309255		2022-09-23 02:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【“FIRE達成”の大人気FPが解説！】 FIREする前に「絶対に知っておきたい保険のこと」 - 年収300万円からのFIRE入門	https://diamond.jp/articles/-/309719	達成	2022-09-23 02:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	『数学ゴールデン』＆『とてつもない数学』の著者が考える数学的センスの正体 - とてつもない数学	https://diamond.jp/articles/-/310094	『数学ゴールデン』『とてつもない数学』の著者が考える数学的センスの正体とてつもない数学圧倒的な才能があるわけではなく第一志望の高校にすら落ちた主人公・小野田が青春の全てを賭けて数学オリンピックを目指すーそんな漫画『数学ゴールデン』が、いま多くの大人たちを虜にしています。	2022-09-23 02:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【中学受験】 志望校の合否に直結する 偏差値以上に大事なものとは？ - 中学受験必勝ノート術	https://diamond.jp/articles/-/310077	安浪京子先生と人のベテラン講師が、中学受験生に向けて、数・国・理・社の教科の正しいノートの作り方を解説し、「こんなちょっとのことで本当に点数があがるなんて」との感激の声が寄せられている話題の書籍『中学受験必勝ノート術』の中から、一部を抜粋し、ご紹介していきます。	2022-09-23 02:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	グーグルが行った「SEO業者の締め出し」対策！ - ブログで５億円稼いだ方法	https://diamond.jp/articles/-/310185	締め出し	2022-09-23 02:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「自ら考える組織、指示待ち組織」上司の指示の出し方に決定的な違い - アジャイル仕事術	https://diamond.jp/articles/-/310136		2022-09-23 02:05:00
北海道	北海道新聞	朝日が注ぐ雲海、幻想的　足寄	https://www.hokkaido-np.co.jp/article/735169/	足寄町里見が丘	2022-09-23 02:06:14
北海道	北海道新聞	入国者上限１０月１１日から撤廃　全国旅行支援も開始　首相会見	https://www.hokkaido-np.co.jp/article/735185/	岸田文雄	2022-09-23 02:02:05