投稿時間:2021-08-20 06:27:26 RSSフィード2021-08-20 06:00 分まとめ(34件)

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TECH	Engadget Japanese	Apple TV+、アシモフ原作の『ファウンデーション』新予告編公開。配信は9月24日開始	https://japanese.engadget.com/apple-tv-foundation-trailer-205038215.html	apple	2021-08-19 20:50:38
TECH	Engadget Japanese	2020年8月20日、自動追尾に対応した「HDペットカメラ」（KX-HDN215）が発売されました：今日は何の日？	https://japanese.engadget.com/today-203018975.html	kxhdn	2021-08-19 20:30:18
AWS	AWS - Webinar Channel	Standardize Application Delivery with AWS Proton - AWS Online Tech Talks	https://www.youtube.com/watch?v=cJ-iZDjuSwc	Standardize Application Delivery with AWS Proton AWS Online Tech TalksPlatform operators know how fast things move and how many services need to coordinate when it comes to shipping code Tracking the interdependencies between services and infrastructure is time consuming and halts productivity in its tracks Meet AWS Proton the first application delivery service for container based and serverless applications Get an up close look at how AWS customers use AWS Proton to manage and maintain their platforms free up their time and control costs You ll learn about new features released at GA view a demo of how to implement AWS Proton for your environment and improve your application delivery process as well as manageability of your architecture Learning Objectives Discover best practices for creating and managing infrastructure as code templates Set up environment and service templates for developer teams self service use Dive deep into multi account and existing environment support To learn more about the services featured in this talk please visit	2021-08-19 20:21:09
AWS	AWS - Webinar Channel	Introduction to Amazon Lightsail Object Storage - AWS Online Tech Talks	https://www.youtube.com/watch?v=VAaOd3B0t0Y	Introduction to Amazon Lightsail Object Storage AWS Online Tech TalksDevelopers use object storage to store static files for backups website content and much more In this tech talk you will learn of a new low cost way to store your static content on Amazon Lightsail You will also learn how you can improve website performance and speed by using object storage and content distribution networks on Lightsail Learning Objectives Learn of Lightsail s latest launch Learn general benefits of storage and different types of storage on AWS Learn how to use object storage with your website To learn more about the services featured in this talk please visit	2021-08-19 20:21:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	親カテゴリに子カテゴリの記事を表示させない	https://teratail.com/questions/355196?rss=all	wordpress	2021-08-20 05:45:19
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	airtableでデータをPostできない	https://teratail.com/questions/355195?rss=all	airtableでデータをPostできないairnbsptableにデータを送りたいのですが、エラーとなってしまい、送信できません。	2021-08-20 05:39:00
海外TECH	DEV Community	What is Kedro	https://dev.to/waylonwalker/what-is-kedro-5b3h	What is KedroKedro is an unopinionated Data Engineering framework that comes with a somewhat opinionated template It gives the user a way to build pipelines that automatically take care of io through the use of abstract DataSets that the user specifies through Catalog entries These Catalog entries are loaded ran through a function and saved by Nodes The order that these Nodes are executed are determined by the Pipeline which is a DAG It s the runner s job to manage the execution of the Nodes This is an updated version of my original what is kedro article Hot TakeIf you are doing a series of operations to data with python especially if you are using something as supported as pandas you should be using a framework that gives you a pipeline as a DAG and abstracts io OrchestratorsLike I said kedro is unopinionated it does determine where or how your data should be ran The kedro team does support the following Orchestrators with very little add on to the base template Argo WorkflowsPrefectKubeflow WorkflowsAWS BatchDatabricks DataSetsDid I say kedro is unopionated Datasets are what allow kedro too be so flexible accross a number of different python objects Any python object can be made into a kedro dataset Kedro comes out of the box with many purpose built DataSets like storing pandas DataFrames to parquet csv or a sql table If kedro does not come with support for the type of python objects you work with don t worry you can for the closest option they support and build your own Or if you do not want to build your own you can use a PickleDataSet for anything CatalogYou will not often be creating your own datasets most of what you need would already be taken care of by the kedro framework What you will need to do is to use the existing DataSets to build your data catalog Kedro takes care of all of the file io for you you simply need to use the catalog to tell kedro what type of DataSet to use and any extra information that DataSet needs Much of the time this is simply a filepath Typically the catalog is specified in yaml format If you are not familiar with yaml I suggest learnxinyminutes com docs yaml as a resource of examples test type pandas CSVDataSet filepath s your bucket test csv Here is the most basic yaml catalog entry taken from the kedro docscars type pandas CSVDataSet filepath data raw company cars csv sep load args save args index False date format Y m d H M decimal Here is a bit more complex example that takes in load args and save argsdocs NodesNodes are a very core part of kedro to build the DAG These nodes are what provides the definition of what catalog entries get passed into which function and output to another catalog entry import pandas as pdimport numpy as npdef clean data cars pd DataFrame boats pd DataFrame gt Dict str pd DataFrame return dict cars df cars dropna boats df boats dropna def halve dataframe data pd DataFrame gt List pd DataFrame return np array split data nodes node clean data inputs cars boats outputs dict cars df clean cars boats df clean boats node halve dataframe clean cars train cars test cars node halve dataframe dict data clean boats train boats test boats Here is an example of three nodes taken from theirdocs PipelineThe kedro Pipeline is a DAG Directed Acyclic Graph It is a graph object that flows in one direction You can slice into the pipeline using a few built in graph method to nodes from nodes to outputs and from inputs You can chain up these method calls since each one returns a new Pipeline object You can also ask a pipline for its edges with inputs and outputs You can also list every dataset along the way with all inputs or all outputs Lastly you can convert it back into a list of nodes with nodes from kedro pipeline import Pipeline node using our nodes from last timPipeline nodes RunnerThe runner is the bridge between kedro and the orchestrators The kedro team provides some basic runners for running pipelines locally built right into the framework but adding on new runners for different orchestrators is done through the use of adding in a new runner to your project HooksKedro allows you to hook into a number of lifecycle methods through the use of the pluggy framework Yes the one that pytest is built on There are a number of different lifecycle methods that allow us to hook in around where kedro is running such as before pipeline run or after catalog loaded LinksNodePipelineRun a pipelinecreate pipelineskedro deployment	2021-08-19 20:38:04
海外TECH	DEV Community	DeepMind’s XLand, Android 12 Beta’s Camera Switches, a Colorism Issue With Face Filters, & more on DevNews!	https://dev.to/devteam/deepmind-s-xland-android-12-beta-s-camera-switches-a-colorism-issue-with-face-filters-more-on-devnews-deo	DeepMind s XLand Android Beta s Camera Switches a Colorism Issue With Face Filters amp more on DevNews Listen to SE of the DevNews podcast today S E DeepMind s XLand Android Beta s Camera Switches a Colorism Issue With Face Filters and a Senior s Robot Companion DevNews   Your browser does not support the audio element x initializing × Hosts saronyitbarek amp joshpuetz Guests Max Jaderberg Senior Staff Research Scientist DeepMind who talked with us about the company s new and exciting AI training tool XLand s aitchison Software Engineer Forem who joined us to talk about Android beta s “Camera Switches which lets users control their phone with facial expressions You can follow DevNews to get episode notifications and listen right in your feed ーor subscribe on your platform of choice Plus if you leave us a review we ll send you a free pack of thank you stickers Details here Quick Listening LinksApple PodcastsSpotifyGoogleStitcherListen NotesTuneInRSS FeedDEV Pods website Acknowledgements levisharpe for producing amp mixing the showOur season five sponsor Scout APM ️We hope you enjoy this episode of DevNews	2021-08-19 20:17:55
海外TECH	DEV Community	Day 7 : Learning Django Fundamentals	https://dev.to/gauravshekhawat/day-7-learning-django-fundamentals-3g7c	Day Learning Django FundamentalsDjango is a python web programming framework which allows us to dynamically generate HTML and CSS for the backend of our website Django combines the static web pages created using HTML and CSS with the endless possibilites of the programming language python leading us to built dynamic web applications Django will allow us to write python code which will dynamically generate HTML and CSS HTTPIt stands for Hyper Text Transfer Protocol which is the protocol for how messages are going to be sent back and forth over the internet Example of a HTTP request method Get tells us that we want to get that particular page simply indicates that we are requesting for the home page HTTP is the version of HTTP which we are using Host tells us what URL we are trying to access the web page for Example of an HTTP response HTTP Status Codes Starting an Django ProjectTo start a django project in the current directory run this command django admin startproject project nameThe short intro to some starter files made by Django is given below manage py We will use this file to be able to execute commands on this django project settings py If we want to change some settings to add features to our application or make modifications to how our application behaves urls py This is the table of contents of all the urls on our web applications which ulitmately a user can visit	2021-08-19 20:17:52
Apple	AppleInsider - Frontpage News	Apple closes Apple Store in Charleston after employee COVID exposure	https://appleinsider.com/articles/21/08/19/apple-closes-apple-store-in-charleston-after-employee-covid-exposure?utm_medium=rss	Apple closes Apple Store in Charleston after employee COVID exposureApple has temporarily shuttered its Apple Store location in Charleston South Carolina after more than retail staff members were exposed to COVID Credit AppleAccording to Apple s retail website the Apple Charleston location is closed until Monday Aug Bloomberg reports that a sign on the door of the brick and mortar store alerts customers to the temporary closure Read more	2021-08-19 20:28:25
海外科学	NYT > Science	This Rattlesnake Dares You to Call Its Bluff	https://www.nytimes.com/2021/08/19/science/rattlesnake-rattle-loud.html	rattlesnakes	2021-08-19 20:31:03
海外TECH	WIRED	On Roblox, Kids Learn It’s Hard to Earn Money Making Games	https://www.wired.com/story/on-roblox-kids-learn-its-hard-to-earn-money-making-games	developers	2021-08-19 20:32:56
ニュース	@日本経済新聞 電子版	韓国の民主化担った「86世代」が反省文　若者世代の批判受け https://t.co/1Bt8oZmyDM	https://twitter.com/nikkei/statuses/1428454428596531203	韓国	2021-08-19 20:30:49
ニュース	@日本経済新聞 電子版	NYダウ続落66ドル安　量的緩和縮小への警戒続く https://t.co/H9NKvvWant	https://twitter.com/nikkei/statuses/1428449889101307917	量的緩和縮小	2021-08-19 20:12:47
ニュース	@日本経済新聞 電子版	「引き出しNISA」というゴール（野尻哲史） https://t.co/LwRzCUKhQi	https://twitter.com/nikkei/statuses/1428447399744135172	野尻哲史	2021-08-19 20:02:54
ニュース	@日本経済新聞 電子版	アドバンテスト、ライバルとの差は資金効率　在庫に死角 https://t.co/oosfJccbNj	https://twitter.com/nikkei/statuses/1428447398687170561	資金	2021-08-19 20:02:53
ニュース	@日本経済新聞 電子版	タリバンがアフガン制圧　バイデン政権、4つの失敗 https://t.co/n40IdwAPdP	https://twitter.com/nikkei/statuses/1428447397651226626	政権	2021-08-19 20:02:53
ニュース	BBC News - Home	Sheffield hotel fall: Boy who died was Afghan refugee	https://www.bbc.co.uk/news/uk-england-south-yorkshire-58269533	afghan	2021-08-19 20:32:23
ニュース	BBC News - Home	Morrisons backs US firm's improved takeover offer	https://www.bbc.co.uk/news/business-58273916	equity	2021-08-19 20:36:07
ニュース	BBC News - Home	Blue singer Lee Ryan tells court he cannot afford £1,500 fine	https://www.bbc.co.uk/news/uk-england-cambridgeshire-58274941	singer	2021-08-19 20:08:57
ニュース	BBC News - Home	Much-changed Tottenham lose play-off first leg in Portugal	https://www.bbc.co.uk/sport/football/58257076	conference	2021-08-19 20:23:01
ニュース	BBC News - Home	One-Day Cup: Glamorgan beat Durham at Trent Bridge to win first knockout trophy	https://www.bbc.co.uk/sport/cricket/58237726	trophy	2021-08-19 20:15:32
ビジネス	ダイヤモンド・オンライン - 新着記事	倒産危険度ランキング【ワースト20社】直撃、「債務超過は解消」「今期は黒字」と大反論 - 廃業急増！倒産危険度ランキング2021	https://diamond.jp/articles/-/279478	債務超過	2021-08-20 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	倒産危険度ランキング2021【ワースト401～493】フジが民放キー局で唯一ランクインした理由 - 廃業急増！倒産危険度ランキング2021	https://diamond.jp/articles/-/279475	上場企業	2021-08-20 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	米中「新冷戦」で日本がとるべき最も有効な経済安保戦略、甘利明氏が提言 - ポストコロナの新世界	https://diamond.jp/articles/-/279455	安保戦略	2021-08-20 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	NTTデータ・NEC・富士通が「ベンダーロックイン破壊」に最後の抵抗！壮絶舞台裏 - ITゼネコンの巣窟 デジタル庁	https://diamond.jp/articles/-/279618	抵抗勢力	2021-08-20 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	雇用調整助成金の“過剰受給”に国が封じ手発動！狙い撃ちは「あの業種」 - Diamond Premium News	https://diamond.jp/articles/-/280024	diamondpremiumnews	2021-08-20 05:07:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ANA・JALの6月旅客人数が超反動増でも、実態値で分かる「惨状」とは？ - コロナで明暗！【月次版】業界天気図	https://diamond.jp/articles/-/279981	前年同期	2021-08-20 05:05:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	企業がエシカル消費に取り組むときに重視すべき３つのポイント	https://dentsu-ho.com/articles/7875	意識調査	2021-08-20 06:00:00
北海道	北海道新聞	緊急事態要請　対応の遅れは許されぬ	https://www.hokkaido-np.co.jp/article/579994/	感染拡大	2021-08-20 05:05:00
ビジネス	東洋経済オンライン	日銀｢強制貯蓄20兆円の取り崩し｣は楽観的すぎる 将来不安の中で｢予備的貯蓄｣が増えていく | 若者のための経済学 | 東洋経済オンライン	https://toyokeizai.net/articles/-/448302?utm_source=rss&utm_medium=http&utm_campaign=link_back	新型コロナウイルス	2021-08-20 05:50:00
ビジネス	東洋経済オンライン	｢全責任とる｣､巨額買収主導のパナ役員の危機感 キーマンが明かすブルーヨンダー買収の内幕 | IT･電機･半導体･部品 | 東洋経済オンライン	https://toyokeizai.net/articles/-/449305?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2021-08-20 05:30:00
ビジネス	東洋経済オンライン	五輪経済効果｢ここまでアテが外れた｣残念な総括 ｢おもしろうてやがて悲しき五輪かな｣ | 政策 | 東洋経済オンライン	https://toyokeizai.net/articles/-/449300?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2021-08-20 05:20:00
GCP	Cloud Blog	How to get better retail recommendations with Recommendations AI	https://cloud.google.com/blog/topics/developers-practitioners/how-get-better-retail-recommendations-recommendations-ai/	How to get better retail recommendations with Recommendations AITime to go outside again I guess I ll need a sun hat Sunscreen Maybe some new sandals What else With the Recommendations AI service I might be reminded to grab a reusable water bottle and a swimsuit Or some after sun aloe lotion Good thing cause I ll need it Photo by Nawartha Nirmal on UnsplashRecommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list This service is part of our full suite of Retail solutions When you integrate with the Retail API you get the benefit of Google s Product Discovery Integrating once to reap the benefits over and over Recommendations is the starting point and you can easily extend into Retail Search and Vision Product Search in the upcoming future  The Recommendations solution is fully managed global scale and powered by deep learning so you can focus on a great shopping experience and let someone else worry about the infrastructure Compared to baseline recommendation systems used by customers Recommendations AI showed double digit uplift in conversion and clickthrough rates in A B experiments controlled by the customers You can optimize for click through conversion or session revenue and fine tune the models to make sure you omit out of stock items or duplicates for example So how does it work and how do you get started Read on and we ll walk you through the pipeline starting with the data you already have to placement in your online store  Formula Data gt Model gt PlacementYou start with your catalog the list of all the things postcards movies pie recipes that you want to show your customers Then you ingest your PII redacted user events this is the historic event data like home page views add to cart events and more along with real time user events This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers activity thus being able to predict what the shopper has a high propensity to purchase next The user events can come from both online activity across devices or offline store purchasesThe recommendation model will return a list of products which are the recommendations The brains of the operation if you will This model is trained using all the data that you ingest using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News that allows us to uncover shopper intent  so it can best predict the right recommendations to show to the right people Every model outputs a list of product identifiers but where do they go They go into placements the spots panels carousels on your customer s journey interacting with your brand that you ve set aside to highlight recommendations A model can send recommendations to one or more placements but each placement only receives information from one recommendation model Your pages will then need to render the products with the right images text or other metadata using the product ID that is returned by the model What do recommendations look like Let s start by browsing our postcard selling website where I ve been buying some vintage California postcards already The recommendations algorithm has caught on to my interest showing me other potential cards to purchase based on my history Put your data to workTo get started we need to bring your data into the recommendation model so it can understand your customers your inventory and your sales patterns  The model takes in the product catalog you use and metadata about those products to better understand nuances in assortment pricing and variables like size and style You might already have this data stored in BigQuery or Merchant Center and hence we provide easy integrations that you can leverage to get started even faster As for the user events don t worry if you already have systems in place to capture web and mobile activity We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager Javascript pixel or even historic events from Cloud Storage BigQuery or using inline API or JSON so you can immediately train the models on this imported data All this allows you to kickstart integrating with Recommendations AI in a matter of days The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with Once your data is ready to go it takes a few days to train the model Next onto making the data work for you Quickly customize your modelSetting up your own recommendations project in the console gives you the ability to choose what sort of model to train based on what recommendations you want to generate and your objective Are you optimizing for click through rate more people click on the recommendation links or products or for conversion rate more people choose or buy what was suggested or revenue Different models can be optimized for different optimization goals the GCP console explains what each one can do and how you can choose to optimize it Let s unpack some of this terminology real quick We ve got three model types Recommended for you  Means we think these are items you ll want to buy based on your history this is usually used on a home page to showcase items Others you may like Means if you re browsing the page of a water bottle we will recommend alternative brands of water bottles that you may like as well as a backpack based on your engagement history Frequently bought together Means that when anyone buys sunscreen we notice that they often also buy aloe lotion so we will surface those items when someone adds any one of them to their cart And then we have three business objectives that the models optimize for Click through rate How frequently did somebody click on a recommended item Conversion rate How frequently did somebody add a recommended item to their cart Revenue per session How much money did the recommendations generate for you Deliver anywhere along the journeyNow that you re all set up in the Retail AI console you can test out the recommendations in the console even before you deploy to production You can integrate Recommendations into your frontend by calling the Predict APIt The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations  On top of that you can use the recommendations for other parts of your customer s journey Email promotions storefront kiosks display ads or follow up notifications can include recommendations based on past activity and cart contents The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process More best practices and guides are available inside our documentation How to get startedTraining your own models can be tedious time consuming and expensive On top of that it requires deeper data science expertise to set up Let us do it instead You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit To get started today you ll need to make a Cloud project and enable the Retail API which then allows you to access all the recommendation tools in one menu Bring in your catalog and purchasing data define a placement or two and you can start putting recommendations on your site in a matter of days Related ArticleIKEA Retail Ingka Group increases Global Average Order Value for eCommerce by with Recommendations AIIKEA uses Recommendations AI to provide customers with more relevant product information Read Article	2021-08-19 20:30:00