投稿時間:2022-06-15 19:43:16 RSSフィード2022-06-15 19:00 分まとめ(42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
ROBOT	ロボスタ	大阪芸術大学 アンドロイドと音楽を科学するラボラトリー「AMSL」を開設 最先端アンドロイド「オルタ４」との記念コンサートを実施	https://robotstart.info/2022/06/15/alter-music-lab-amsl.html		2022-06-15 09:32:43
IT	ITmedia 総合記事一覧	[ITmedia Mobile] Nothing、7月発表予定の「phone(1)」の背面画像をツイートでチラ見せ	https://www.itmedia.co.jp/mobile/articles/2206/15/news199.html	itmediamobilenothing	2022-06-15 18:48:00
IT	ITmedia 総合記事一覧	[ITmedia Mobile] 総務省が携帯キャリアと販売代理店団体に「要請」　携帯電話の販売に関する業務の適正性確保を求める	https://www.itmedia.co.jp/mobile/articles/2206/15/news198.html	itmediamobile	2022-06-15 18:45:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	国内企業を狙う標的型攻撃、多くの企業が侵入に気づかず事態が深刻化─マクニカ調査 | IT Leaders	https://it.impress.co.jp/articles/-/23329	国内企業を狙う標的型攻撃、多くの企業が侵入に気づかず事態が深刻化ーマクニカ調査ITLeadersマクニカは年月日、年度の日本における標的型攻撃に関する調査レポート「標的型攻撃の実態と対策アプローチ第版」を公開した。	2022-06-15 18:40:00
AWS	AWS Japan Blog	AWS トラベルと ホスピタリティパートナー (IBS Software) の対談	https://aws.amazon.com/jp/blogs/news/aws-travel-and-hospitality-partner-conversations-ibs-software/	anandkrishnan	2022-06-15 09:11:30
python	Pythonタグが付けられた新着投稿 - Qiita	PythonとHerokuで最初のRedditボットを作成する	https://qiita.com/Oukaria/items/35977bd5997503d43911	heroku	2022-06-15 18:59:31
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Selenium】Dockerコンテナ内でSeleniumを実行すると起きるエラーの解決: Selenium::WebDriver::Error::UnknownError【ruby】	https://qiita.com/at_sushi/items/41d3d3195e1d989ec72b	【Selenium】Dockerコンテナ内でSeleniumを実行すると起きるエラーの解決SeleniumWebDriverErrorUnknownError【ruby】ソースコードだいぶはしょってますが、クロールするための、Crawlerクラスです。	2022-06-15 18:23:50
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Ruby で開発する時に、少し助けになる（かも知れない）小ネタ	https://qiita.com/shima-akira/items/c0ddfbb31e3361d89d77	qiita	2022-06-15 18:21:04
AWS	AWSタグが付けられた新着投稿 - Qiita	クラウド実務経験なしエンジニアによるAWS SOA合格体験記（ラボ対策）	https://qiita.com/shaft5280/items/b8505b11e02cf0a39c7e	awssoa	2022-06-15 18:54:44
AWS	AWSタグが付けられた新着投稿 - Qiita	ECS FargateにSSMを利用してSSH接続する(チュートリアル)	https://qiita.com/kouji0705/items/005ea6d7c21ddd24ebb3	fargate	2022-06-15 18:52:34
Docker	dockerタグが付けられた新着投稿 - Qiita	【Selenium】Dockerコンテナ内でSeleniumを実行すると起きるエラーの解決: Selenium::WebDriver::Error::UnknownError【ruby】	https://qiita.com/at_sushi/items/41d3d3195e1d989ec72b	【Selenium】Dockerコンテナ内でSeleniumを実行すると起きるエラーの解決SeleniumWebDriverErrorUnknownError【ruby】ソースコードだいぶはしょってますが、クロールするための、Crawlerクラスです。	2022-06-15 18:23:50
Git	Gitタグが付けられた新着投稿 - Qiita	素人なりに考えたGithubでのチーム開発運用法	https://qiita.com/infoengine1337/items/d2f5aff1ae0f04df29d9	github	2022-06-15 18:26:38
Ruby	Railsタグが付けられた新着投稿 - Qiita	Railsの絞り込みルーティング検索	https://qiita.com/tonmaruki/items/27a0a7d2866ac761504e	rails	2022-06-15 18:43:07
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】コントローラーにたくさんコードを書いてしまった・・・そんな時はモデルに処理を寄せよう！	https://qiita.com/yuuyake9191/items/da7dee098935e2420fe3	defaabca	2022-06-15 18:25:29
技術ブログ	Developers.IO	CDK Appをv1からv2に移行するモチベーションまとめ	https://dev.classmethod.jp/articles/motivations-to-migrate-from-cdk-v1-to-v2/	cdkapp	2022-06-15 09:38:25
技術ブログ	Developers.IO	Cloudflare WAF のZone Lockdown を利用して送信元IPアドレスのアクセス制限を実施する	https://dev.classmethod.jp/articles/cloudflare-waf-zone-lockdown/	cloudflare	2022-06-15 09:35:00
技術ブログ	Developers.IO	Aqua Platform Enterprise MicroEnforcerのサイドカーでFargateに起動したDVWAを防御してみた	https://dev.classmethod.jp/articles/aqua-platform-enterprise-ecs-fargate-microenforcer-sidecar/	aquaplatform	2022-06-15 09:08:41
海外TECH	MakeUseOf	This Is What the Nothing Phone (1) Looks Like	https://www.makeuseof.com/this-is-what-the-nothing-phone-1-looks-like/	debut	2022-06-15 09:30:13
海外TECH	DEV Community	Quickly develop risk control algorithms in business scenarios based on MetaSpore	https://dev.to/qazmkop/quickly-develop-risk-control-algorithms-in-business-scenarios-based-on-metaspore-3c1c	Quickly develop risk control algorithms in business scenarios based on MetaSporeMetaSpre AlphaIDE After decades of development the traditional credit evaluation model for risk management is relatively mature and stable Represented by the FICO score in the United States it builds a rule engine and promotes the rapid development of the financial loan business in the United States In recent years with the leapfrog development of big data and artificial intelligence technologies financial institutions can draw diversified user portraits and build more accurate risk control models under the support of new technologies This paper will take the Tianchi loan default data set as an example train and evaluate the default prediction model in MetaSpore on the AlphaIDE development environment launched by DMetaSoul and give intelligent credit score according to the estimated probability In the following chapters I will focus on environmental use problem modeling feature derivation modeling score cards and so on MetaSpore On AlphaIDE IDE environment configuration and startupRegister or log in AlphaIDE account Enter the AlphaIDE service click application service on the left click Kubeflow drop down menu and enter the Jupyter page Click on the upper right to create the Notebook and select the desired resource under CPU RAM It is recommended to use Core CPU X GB or more of RAM Check Kubeflow AWS and Spark and then Launch to create the Notebook After creating the Juyper Notebook click Connect to open a Terminal and run git clone git github com meta soul MetaSpore git Run the algorithm Demo and develop it after the completion of the clone code base Training machine learning modelsSee the algorithms project in MetaSpore s Demo directory for recommendations search NLP and risk control related applications Here take the risk control project Demo RiskModels Loan default as an example Star the Spark Session If using Spark in the Alpha IDE distributed cluster training you need to increase the Spark Kubernetes Namespace configuration parameters such as def init spark app name cluster namespace kwargs spark pyspark sql SparkSession builder appName app name config spark kubernetes namespace cluster namespace getOrCreate sc spark sparkContext print sc version print sc applicationId print sc uiWebUrl return sparkOf course you can also run in local mode by changing the fourth line above to master local When the number of samples is small the local mode is better Start the model training script follow the steps in the readme md document to prepare the training data initialize the model configuration file we need and then execute the training script Save the model training results Save the trained model and estimated results to S cloud storage as we did in default ESTIMation spark lgbm py It may take some time to download the dependent libraries during the first execution Intelligent risk control algorithm BackgroundBefore diving into the actual code implementation let s take a quick look at the data set The data set given by the Tianchi community is used in this article The data set is to predict whether the user defaults on loans The data comes from the loan records of A credit platform with A total of over million data including columns of variable information unknown variables pieces as the test set A and pieces as test set B isDefault in the dataset can be used as the training label Other columns can be used as the model s features including ID category and numerical features The complete introduction of feature columns can refer to the description of the dataset provided by the government and the following figure shows the sample data The so called default rate prediction is to use the machine learning model to establish the learner of binary classification that is through the model to estimate When the risk control algorithm is implemented the model s accuracy and interpretability should be considered simultaneously The linear model and tree model are generally used The following examples are also based on the LightGBM model Based on the default rate prediction model we can establish a user s credit score similar to the sesame credit score of Ant Group Generally speaking the lower the default probability is the higher our score should be which is convenient for loan personnel to evaluate customers Feature EngineeringThe evaluation problems related to financial loans are mainly based on tabular data so the importance of feature engineering is self evident The common features in the dataset include ID type Categorical type and continuous number type which require common data handling such as EDA missing value completion outlier processing normalization feature binning and importance assessment The process can reference the GitHub codebase which part about tianchi loan instructions WoE Weight of Evidence coding is required for feature derivation for risk control models especially for standard risk control scorecards WoE represents the difference in the proportion of good and bad customers in the current feature binning smoothed by the log function and calculated by the following formula Among them py pn respectively represent the percentage of good or bad customers in the total good or bad customers in this binning Badi Badt respectively represent the number of bad customers in the current binning and the number of bad customers in all customers Goodi Goodt respectively represent the number of good customers in the current binning and the number of good users in all customers If the larger the absolute value of WoE is the more significant the difference is the feature binning is a perfect predictor On the other hand If WoE is then the ratio of good and bad customers is equal to the percentage of random bad and good customers In this case WoE has no predictive power The calculation process In actual business scenarios a lot of work will focus on feature binning and filtering The commonly used methods in the process of feature binning include isofrequency isometric Best KS ChiMerge and other methods In the process of feature screening the chi square test wrapper based on tree model and IV Information Value based on WoE are the common methods However these tasks require a lot of data analysis and iteration for specific businesses Model trainingOnce the samples and features are ready we can train the model Here we use the Spark version of the LightGBM model which is both model performance and interpretable and compatible with AlphaIDE and MetaSpore Serving Model training code Another advantage of using the Spark version of the machine learning model is that it can fully use cluster computing resources during hyperparameter optimization To demonstrate use Hyperopt to optimize the combination of learningRate and numIterations as two super parameters Define the optimization space of the hyperparameters as follows After the train with HYPERopt function is defined optimize the parameter combination The optimization process is slow When finish the execution best params can be output to view the best parameter combination In practical business hyperparameter optimization will have more space and consume more computing time After the hyperparameter optimization is completed the model can be trained again on the full sample set After the training we can export the model to ONNX format For details please refer to the introduction in or the export code given in our GitHub code repository Model evaluationIn addition to the commonly used AUC index to evaluate the performance of the risk control algorithm model the Kolmogorov Smirnov index is another measure The higher the KS index is the stronger the risk differentiation ability of the model is The business significance of the KS curve is that we allow the model to make a small number of errors in exchange for maximizing the identification of bad samples In general KS lt the differentiation ability of the model is not high and the application value is not high lt KS lt general models are concentrated in this interval so it is necessary to continue to observe the tuning model lt KS lt The model has good differentiation ability and strong application value KS lt there may be a fitting phenomenon that needs to be checked We provide a KS value calculation and KS drawing toolbox in MetaSpore After we write the test results into S cloud storage we can call the functions in the toolbox to evaluate the model Credit ScoreAssuming that we have reasonably estimated the loan default rate through the machine learning model we can give the following score If the following two assumptions are satisfied then we can derive the calculation formula of constants A and B A Initial value hypothesis The score is assumed at S i e B Point of Double assumption Assume that odds ×oddso a fixed PDO score reduces the credit score The formula for calculating A and B Here is the result of the scorecard operation The results showed that the lower the probability of default the higher the credit score Refer to the GitHub repository for code implementation The results of the score can also be evaluated using the KS indicator In addition it should be noted that to implement a standard scorecard is to be implemented a linear model usually Logistic Regression is required Besides a credit score for each user the linear model s intercept and the binning of each one dimensional feature need to be scored ConclusionDMetaSoul uses MetaSpore on AlphaIDE to quickly implement a loan default rate prediction model on an open source dataset and build a scorecard based on this model Based on the Demo system of this version the methods of feature derivation binning and screening can be more delicate which often determines the upper limit of the performance of the risk control system Finally give the address of the code base and the AlphaIDE trial link AlphaIDE tutorial Default rate forecast MetaSpore s one stop machine learning development platform AlphaIDE trial link	2022-06-15 09:26:30
金融	RSS FILE - 日本証券業協会	動画で見る日証協の活動	https://www.jsda.or.jp/about/gaiyou/movie/index.html	日証協	2022-06-15 10:00:00
金融	RSS FILE - 日本証券業協会	会員の決算概況	https://www.jsda.or.jp/shiryoshitsu/toukei/kessan/index.html	決算	2022-06-15 09:30:00
海外ニュース	Japan Times latest articles	Japan to form its own CDC-like body to confront viruses of the future	https://www.japantimes.co.jp/news/2022/06/15/national/kishida-infectious-disease-outbreak-agency/	Japan to form its own CDC like body to confront viruses of the futureExperts say the speed of government responses are hindered without a “control tower to address outbreaks and barriers between ministries	2022-06-15 18:33:55
海外ニュース	Japan Times latest articles	On climate change’s front lines, hard lives grow even harder	https://www.japantimes.co.jp/news/2022/06/15/world/climate-change-hard-lives-harder/	On climate change s front lines hard lives grow even harderHundreds of millions of humanity s most vulnerable live in South Asia where rising temperatures make it more difficult to address poverty food security and health	2022-06-15 18:02:03
海外ニュース	Japan Times latest articles	Net-zero Picasso: Museums rethink art shows to cut climate impact	https://www.japantimes.co.jp/culture/2022/06/15/entertainment-news/art-museums-climate-change/	travel	2022-06-15 18:09:05
ニュース	BBC News - Home	Rwanda asylum: Government plans new flight after cancellation	https://www.bbc.co.uk/news/uk-61808120?at_medium=RSS&at_campaign=KARANGA	challenge	2022-06-15 09:55:47
ニュース	BBC News - Home	EU takes new legal action against UK over post-Brexit deal changes	https://www.bbc.co.uk/news/uk-politics-61809459?at_medium=RSS&at_campaign=KARANGA	action	2022-06-15 09:25:54
ニュース	BBC News - Home	Millions to get first cost-of-living payment from 14 July	https://www.bbc.co.uk/news/business-61802109?at_medium=RSS&at_campaign=KARANGA	julymore	2022-06-15 09:19:49
ニュース	BBC News - Home	Northern Ireland Protocol: Is Truss right about public opinion?	https://www.bbc.co.uk/news/61798013?at_medium=RSS&at_campaign=KARANGA	brexit	2022-06-15 09:50:39
ニュース	BBC News - Home	Joe Root: England batsman returns to top of Test rankings	https://www.bbc.co.uk/sport/cricket/61808048?at_medium=RSS&at_campaign=KARANGA	batsman	2022-06-15 09:10:33
GCP	Google Cloud Platform Japan 公式ブログ	承認済みネットワークと GKE 上の Cloud Run / Functions に関連するアップデートを間もなくリリース	https://cloud.google.com/blog/ja/products/identity-security/updates-coming-for-authorized-networks-and-cloud-runfunctions-on-gke/	残りの限定公開クラスタについては、新しいソリューションにアクセスを移行していただく時間が必要となるため、お客様のご都合に合わせて移行を進めていただきます。	2022-06-15 10:59:00
GCP	Google Cloud Platform Japan 公式ブログ	Google Cloud が Cloud Digital Leader プログラムで高等教育をサポート	https://cloud.google.com/blog/ja/topics/training-certifications/cloud-digital-leader-curriculum-is-now-available-for-faculty/	CloudDigitalLeaderキャリア教育トラックCloudDigitalLeaderキャリア教育トラックは、学生がCloudDigitalLeader認定資格の取得に向けて準備するために必要なリソースを、対象となる教職員に提供することを目的としています。	2022-06-15 10:55:00
GCP	Google Cloud Platform Japan 公式ブログ	Palexy が Google Cloud を活用して小売店の店内売り上げ向上を支援	https://cloud.google.com/blog/ja/topics/startups/built-on-google-cloud-palexy-perfects-the-in-store-customer-journey/	店内のカスタマージャーニーを完璧なものにするGoogleforStartupsクラウドプログラムのおかげで、当社は世界中の何千もの店舗で使用されている包括的な小売分析プラットフォームを迅速に構築することができました。	2022-06-15 10:54:00
GCP	Google Cloud Platform Japan 公式ブログ	クラウド内の AI 構築: Google Cloud と NVIDIA で簡単に	https://cloud.google.com/blog/ja/products/ai-machine-learning/how-nvidia-and-google-cloud-accelerate-ml-deployments/	GoogleCloudと共同開発したこの機能を使用すると、AIソフトウェアをNGCカタログからワンクリックで簡単にデプロイできます。	2022-06-15 10:50:00
GCP	Google Cloud Platform Japan 公式ブログ	機械学習の概要: 職種別・タスク別推奨リソース 25 選以上	https://cloud.google.com/blog/ja/products/ai-machine-learning/getting-started-with-vertex-ai/	モデルレジストリ動画AIMLNotebookshowtowithApacheSparkBigQueryMLand VertexAIModelRegistryAIMLノートブックApacheSpark、BigQueryML、VertexAIのモデルレジストリと連携する方法モデルのトレーニングCodelabVertexAIWorkbenchノートブックエグゼキュータによるモデルのトレーニングCodelab自動パッケージ化を使用してVertexAITrainingのHuggingfaceでBERTを微調整ブログVertexAIでPyTorchモデルのトレーニングと調整を行う方法大規模なモデルのトレーニングCodelabTensorFlowを使用したマルチワーカーのトレーニングと転移学習ブログVertexAIのReductionServerを使用してトレーニングのパフォーマンスを最適化する動画DistributedtrainingonVertexAIWorkbenchVertexAIWorkbenchの分散トレーニングモデルのチューニングCodelabハイパーパラメータ調整動画VertexAIでモデルのトレーニングと実験を高速化するモデルの提供ブログVertexAIにPyTorchモデルをデプロイする方法ブログstepstogofromanotebooktoadeployedmodelノートブックからデプロイ済みモデルに移行するつのステップMLエンジニア以下にMLエンジニア向けのリソースを示します。	2022-06-15 10:42:00
ビジネス	東洋経済オンライン	FRBの今回の利上げは0.75％にとどまるのか？ 市場が今後警戒すべきなのは金利だけではない | 市場観測 | 東洋経済オンライン	https://toyokeizai.net/articles/-/597081?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2022-06-15 18:30:00
IT	週刊アスキー	『P3P』『P4G』『P5R』の「ペルソナ」3作品がXbox、PS、PCフォーマットに続々登場！	https://weekly.ascii.jp/elem/000/004/094/4094826/	esxsxboxonepcwindowssteam	2022-06-15 18:50:00
GCP	Cloud Blog	Ciao, Milano! New cloud region in Milan now open	https://cloud.google.com/blog/products/infrastructure/new-google-cloud-region-in-milan-italy-now-open/	Ciao Milano New cloud region in Milan now openToday we are excited to celebrate the opening of our new Google Cloud region in Milan in partnership with TIM You can now store data and leverage fast reliable and secure cloud services to build and deliver highly available low latency applications for your customers ーall on the cleanest cloud in the industry Fantastico no  The new Milan region europe west is now part of our global network of regions and zones bringing Google Cloud services to users in over countries and territories worldwide It is the first of two regions that are opening in Italy  the second will launch in Turin in the coming months We believe the new cloud regions in Milan and Turin will have a positive impact for years to come A recent independent economic impact report made by the University of Turin highlights that the two regions could potentially generate up to € bn and support up to new jobs in Piedmont and Lombardy by Embracing the cloud Italian styleThe addition of two Italian cloud regions starting with Milan is not only part of our commitment to aid in the digital recovery of Italy but to continue expanding our digital footprint Built in collaboration with Telecom Italia TIM the new Milan region aims to provide innovative public private and hybrid cloud services to help Italian companies of all sizes and across all industries accelerate digital transformation  Organizations everywhere need the capacity to run mission critical applications at the speed their customers expect This new region is a strong step towards building regional capacity that meets the needs of the Italian digital economy from availability and data residency to digital sovereignty and sustainability  “The partnership between TIM and Google Cloud will accelerate the digitization of companies public administrations and private citizens Customers will benefit from innovative flexible and secure digital solutions thanks to the new Italian Google Cloud regions and TIM s sustainable data centers In fact we are inaugurating a strategic infrastructure that will combine the huge economic benefits expected in Lombardy and Piedmont with important advantages for the environment our network of data centers designed and built according to eco sustainability criteria will allow significant CO savings said Elio Schiavo Chief Enterprise and Innovative Solutions Officer TIM Cloud on Italy s termsThe new Milan region launches with three cloud zones and our standard services including Compute Engine Google Kubernetes Engine Cloud Storage Persistent Disk CloudSQL and Cloud Identity In addition our customers will benefit from critical features such as data residency controls default encryption organizational policies and VPC Service Controls Like all Google Cloud regions the Milan region is connected to Google s secure backbone comprising a system of high capacity fiber optic cables under land and sea around the world In addition multiple regions in the same country will offer Google Cloud customers a secondary local site to ensure better disaster recovery or geographical high availability that allows them to meet business continuity requirements  “The availability of two Google Cloud regions in Italy is a strategic asset for Intesa Sanpaolo The Cloud will in fact become a real extension of our information system allowing us a rapid digitization of critical processes with significant competitive advantages in fact we will be able to serve families and businesses more and more quickly with innovative tools easy to use and in line with the growing digitization needs of the country positioning us as a leader also in the technological field  commented Enrico Bagnasco Executive Director IT Head Office Department Intesa SanpaoloHelping Italy build the future with a transformation cloudOur mission at Google Cloud is to provide a platform that enables companies in every industry and any country to transform the way they do business and serve their customers using digital technology  Whether you re a traditional enterprise a startup or a digital native we re ready to help propel you to the next level with a cloud built to open new doors to opportunity right from the economic heart of Italy  Our transformation cloud is helping businesses become  Smarter The power of data is helping companies leverage more data and deeper insights to help catalyze transformation Google Cloud makes it easy to get more value from structured or unstructured data to solve your most important challenges make data driven decisions and deliver better business outcomes  Open Google Cloud s design principles are deeply rooted in the belief that a secure open approach is the best way to empower you to drive differentiated experiences Our commitment to multicloud hybrid cloud and open source unlocks innovation so you can build faster in any environment  Connected Digital transformation goes beyond technologyーit requires the right people and culture As work continues to shift outside of physical locations Google Cloud provides the tools to help people be more innovative productive and able to make faster decisions  Trusted Google Cloud offers a zero trust architecture to protect data applications and infrastructure and a shared responsibility model where we have an active stake in our customers security outcomes We are also trusted partner of European businesses and governments so you can transform faster while also meeting digital sovereignty requirements Sustainable Google Cloud matches of the electricity that powers your workloads with renewable energy purchases enabling us to operate the cleanest cloud in the industry With our Carbon Sense suite of products you can measure report and reduce the carbon footprint of your cloud usage  In addition to the launch of our new Milan cloud region we ve partnered with TIM to launch Google Cloud Pro in a free developer training program for Google Cloud that is open to the entire ecosystem of Italian developers We also partnered with TIM Noovle and Intesa Sanpaolo to help Italian entrepreneurs continue to grow with initiatives such as the Opening Future project dedicated to promoting digital skills for startups students and small businesses Visit our locations page for more details about Google Cloud regions and updates on the availability of additional services and regions  The Economic and Employment Impact of Milan Data Centers Francesco Carbonero University of Turin Aldo Geuna University of Turin Luigi Riso Catholic University of Milan May Related ArticleCloud on Spain s terms New Google Cloud region in Madrid now openThe new Madrid region europe southwest provides low latency highly available services with international security and data protection Read Article	2022-06-15 09:30:00
GCP	Cloud Blog JA	承認済みネットワークと GKE 上の Cloud Run / Functions に関連するアップデートを間もなくリリース	https://cloud.google.com/blog/ja/products/identity-security/updates-coming-for-authorized-networks-and-cloud-runfunctions-on-gke/	残りの限定公開クラスタについては、新しいソリューションにアクセスを移行していただく時間が必要となるため、お客様のご都合に合わせて移行を進めていただきます。	2022-06-15 10:59:00
GCP	Cloud Blog JA	Google Cloud が Cloud Digital Leader プログラムで高等教育をサポート	https://cloud.google.com/blog/ja/topics/training-certifications/cloud-digital-leader-curriculum-is-now-available-for-faculty/	CloudDigitalLeaderキャリア教育トラックCloudDigitalLeaderキャリア教育トラックは、学生がCloudDigitalLeader認定資格の取得に向けて準備するために必要なリソースを、対象となる教職員に提供することを目的としています。	2022-06-15 10:55:00
GCP	Cloud Blog JA	Palexy が Google Cloud を活用して小売店の店内売り上げ向上を支援	https://cloud.google.com/blog/ja/topics/startups/built-on-google-cloud-palexy-perfects-the-in-store-customer-journey/	店内のカスタマージャーニーを完璧なものにするGoogleforStartupsクラウドプログラムのおかげで、当社は世界中の何千もの店舗で使用されている包括的な小売分析プラットフォームを迅速に構築することができました。	2022-06-15 10:54:00
GCP	Cloud Blog JA	クラウド内の AI 構築: Google Cloud と NVIDIA で簡単に	https://cloud.google.com/blog/ja/products/ai-machine-learning/how-nvidia-and-google-cloud-accelerate-ml-deployments/	GoogleCloudと共同開発したこの機能を使用すると、AIソフトウェアをNGCカタログからワンクリックで簡単にデプロイできます。	2022-06-15 10:50:00
GCP	Cloud Blog JA	機械学習の概要: 職種別・タスク別推奨リソース 25 選以上	https://cloud.google.com/blog/ja/products/ai-machine-learning/getting-started-with-vertex-ai/	モデルレジストリ動画AIMLNotebookshowtowithApacheSparkBigQueryMLand VertexAIModelRegistryAIMLノートブックApacheSpark、BigQueryML、VertexAIのモデルレジストリと連携する方法モデルのトレーニングCodelabVertexAIWorkbenchノートブックエグゼキュータによるモデルのトレーニングCodelab自動パッケージ化を使用してVertexAITrainingのHuggingfaceでBERTを微調整ブログVertexAIでPyTorchモデルのトレーニングと調整を行う方法大規模なモデルのトレーニングCodelabTensorFlowを使用したマルチワーカーのトレーニングと転移学習ブログVertexAIのReductionServerを使用してトレーニングのパフォーマンスを最適化する動画DistributedtrainingonVertexAIWorkbenchVertexAIWorkbenchの分散トレーニングモデルのチューニングCodelabハイパーパラメータ調整動画VertexAIでモデルのトレーニングと実験を高速化するモデルの提供ブログVertexAIにPyTorchモデルをデプロイする方法ブログstepstogofromanotebooktoadeployedmodelノートブックからデプロイ済みモデルに移行するつのステップMLエンジニア以下にMLエンジニア向けのリソースを示します。	2022-06-15 10:42:00