投稿時間:2022-11-30 03:42:28 RSSフィード2022-11-30 03:00 分まとめ(48件)

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AWS	AWS News Blog	New – Amazon EC2 Hpc6id Instances Optimized for High Performance Computing	https://aws.amazon.com/blogs/aws/new-amazon-ec2-hpc6id-instances-optimized-for-high-performance-computing/	New Amazon EC Hpcid Instances Optimized for High Performance ComputingWe have given you the flexibility and ability to run the largest and most complex high performance computing HPC workloads with Amazon Elastic Compute Cloud Amazon EC instances that feature enhanced networking like Cn Cgn nbsp Rn Mn and nbsp our recently launched HPC instances Hpca We heard feedback from customers asking us to deliver more options to support …	2022-11-29 17:49:24
AWS	AWS News Blog	Preview: Amazon Security Lake – A Purpose-Built Customer-Owned Data Lake Service	https://aws.amazon.com/blogs/aws/preview-amazon-security-lake-a-purpose-built-customer-owned-data-lake-service/	Preview Amazon Security Lake A Purpose Built Customer Owned Data Lake ServiceTo identify potential security threats and vulnerabilities customers should enable logging across their various resources and centralize these logs for easy access and use within analytics tools Some of these data sources include logs from on premises infrastructure firewalls and endpoint security solutions and when utilizing the cloud services such as Amazon Route AWS CloudTrail …	2022-11-29 17:31:21
AWS	AWS News Blog	New – Amazon Redshift Integration with Apache Spark	https://aws.amazon.com/blogs/aws/new-amazon-redshift-integration-with-apache-spark/	New Amazon Redshift Integration with Apache SparkApache Spark is an open source distributed processing system commonly used for big data workloads Spark application developers working in Amazon EMR Amazon SageMaker and AWS Glue often use third party Apache Spark connectors that allow them to read and write the data with Amazon Redshift These third party connectors are not regularly maintained supported or tested with …	2022-11-29 17:11:29
AWS	AWS Big Data Blog	New analytical questions available in Amazon QuickSight Q: “Why” and “Forecast”	https://aws.amazon.com/blogs/big-data/new-analytical-questions-available-in-amazon-quicksight-q-why-and-forecast/	New analytical questions available in Amazon QuickSight Q “Why and “Forecast Amazon QuickSight Q uses machine learning ML to enable any user to ask questions about business data in natural language and receive accurate answers with relevant visualizations in seconds Today Amazon QuickSight announces support for two new question types that simplify and scale complex analytical tasks using natural language “forecast and “why In this post …	2022-11-29 17:20:37
AWS	AWS Government, Education, and Nonprofits Blog	Now available: New AWS program supporting nonprofit donor and member engagement	https://aws.amazon.com/blogs/publicsector/techaction-new-aws-program-supporting-nonprofit-donor-member-engagement/	Now available New AWS program supporting nonprofit donor and member engagementAs one time donations increasingly become the norm nonprofit development teams are challenged to think outside the box to attract retain and communicate with their valuable supporters Nonprofit organizations can use data to inform an enhanced engagement strategy but many are challenged to unlock the full value of that data affordably and at scale To help nonprofits use the cloud to build innovative fundraising and member engagement solutions we are launching a new program AWS TechAction	2022-11-29 17:57:02
AWS	AWS Government, Education, and Nonprofits Blog	Amazon and George Mason University collaborate to deliver new innovations in education and research	https://aws.amazon.com/blogs/publicsector/amazon-george-mason-university-collaborate-deliver-new-innovations-education-research/	Amazon and George Mason University collaborate to deliver new innovations in education and researchGeorge Mason University Mason is on a mission to build a more student centric and technologically advanced education institution Leveraging the breadth and depth of services across Amazon including technical solutions from Amazon Web Services AWS the university will embark on a multi year collaborative effort Mason will drive new innovations to better serve students work with the defense community advance its commitment to a more sustainable campus environment and introduce new educational programs in data center engineering	2022-11-29 17:24:27
AWS	AWS Japan Blog	Amazon CloudWatch Internet Monitor プレビュー – アプリケーションのインターネットパフォーマンスをエンドツーエンドで可視化	https://aws.amazon.com/jp/blogs/news/cloudwatch-internet-monitor-end-to-end-visibility-into-internet-performance-for-your-applications/	amazon	2022-11-29 17:52:16
AWS	AWS Japan Blog	AWS re:Invent 2022に関する注目の発表	https://aws.amazon.com/jp/blogs/news/top-announcements-of-aws-reinvent-2022/	awsreinvent	2022-11-29 17:41:01
AWS	AWS	Amazon EC2 Hpc6id instances | Amazon Web Services	https://www.youtube.com/watch?v=E06ALZIXdQ4	Amazon EC Hpcid instances Amazon Web ServicesAmazon Elastic Compute Cloud Amazon EC Hpcid instances powered by rd Generation Intel Xeon Scalable processors offer cost effective price performance for memory bound and data intensive high performance computing HPC workloads in Amazon EC These instances are built on the AWS Nitro System providing Gbps for low latency inter node communication Learn more Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster HPC EC AWS AmazonWebServices CloudComputing	2022-11-29 17:56:13
AWS	AWS	What is Amazon Security Lake? | Amazon Web Services	https://www.youtube.com/watch?v=j6IxULE7_4k	What is Amazon Security Lake Amazon Web ServicesAmazon Security Lake automatically centralizes your security data into a data lake stored in your account You can use Security Lake to more easily gather and analyze your security data from cloud on premises and custom sources to improve the protection of your workloads applications and data Learn more Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-11-29 17:39:17
AWS	AWS	Amazon DataZone Portal | Amazon Web Services	https://www.youtube.com/watch?v=n65ij6OxoPo	Amazon DataZone Portal Amazon Web ServicesAmazon DataZone provides access to analytics via a web application enabling analyst and line of business end users to use data without domain knowledge of AWS products under the hood Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-11-29 17:27:33
AWS	AWS	Amazon DataZone Catalog  | Amazon Web Services	https://www.youtube.com/watch?v=iDhr1X3MxBg	Amazon DataZone Catalog  Amazon Web ServicesAmazon DataZone introduces a business data catalog that allows customers to catalog share search and discover data assets across multiple sources accounts and regions from data stored in AWS on premises and third party providers like SaaS applications Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-11-29 17:27:10
AWS	AWS	Amazon DataZone Projects | Amazon Web Services	https://www.youtube.com/watch?v=8zhLu2bTJm4	Amazon DataZone Projects Amazon Web ServicesAmazon DataZone introduces data projects for users to collaborate on business case cases through data assets With Data Projects analytics users can collaborate by creating use case based groupings of teams tools and data increasing efficiency and team collaboration Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-11-29 17:26:52
AWS	AWS	Amazon Redshift Integration for Apache Spark | Amazon Web Services	https://www.youtube.com/watch?v=MAxy5MsOun8	Amazon Redshift Integration for Apache Spark Amazon Web ServicesAmazon Redshift Integration for Apache Spark makes it easy to access enriched and highly curated data in Amazon Redshift through Apache Spark applications Get meaningful insights faster in AWS analytics services with this AWS certified connector Get started with Amazon Redshift Integration for Apache Spark today Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster Redshift AWS AmazonWebServices CloudComputing	2022-11-29 17:25:30
AWS	AWS	Amazon OpenSearch Serverless | Amazon Web Services	https://www.youtube.com/watch?v=aCROb9Ggqxc	Amazon OpenSearch Serverless Amazon Web ServicesAmazon OpenSearch Serverless automatically provisions and scales resources to provide consistently fast data ingest rates and millisecond response times for even the most demanding and unpredictable workloads OpenSearch Serverless makes it significantly easier and more cost effective for customers to modernize their infrastructure and analyze vast amounts of data without worrying about capacity commitments or incurring excess costs by over provisioning for peak demand There are no upfront commitments or additional costs to use OpenSearch Serverless Learn more Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-11-29 17:04:52
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Fly.ioにRails製アプリをデプロイした際のエラー解決（Windows）	https://qiita.com/4mami/items/088851ba8f1fdc6e70e7	rubyrubyversiongtrubyp	2022-11-30 02:36:40
Ruby	Railsタグが付けられた新着投稿 - Qiita	Fly.ioにRails製アプリをデプロイした際のエラー解決（Windows）	https://qiita.com/4mami/items/088851ba8f1fdc6e70e7	rubyrubyversiongtrubyp	2022-11-30 02:36:40
技術ブログ	Developers.IO	【速報】コンテナランタイムの驚異検知がGuardDutyで可能になりました！	https://dev.classmethod.jp/articles/container-runtime-threatdetection-guardduty/	inerruntimethreatdetecti	2022-11-29 17:54:24
技術ブログ	Developers.IO	[速報]データマネジメントを実現するAmazon DataZoneがついに登場しました！ #reinvent	https://dev.classmethod.jp/articles/amazon-datazone-announced/	adamselipsky	2022-11-29 17:53:04
技術ブログ	Developers.IO	【速報】BIツールに話しかけたら予測もしてくれる。ML-powered forecasting with Qがリリース。 #reinvent	https://dev.classmethod.jp/articles/breaking-ml-powered-forecasting-with-q/	mlpoweredforecastingwithq	2022-11-29 17:49:44
技術ブログ	Developers.IO	[アップデート] ゼロETL の Amazon Aurora と Amazon Redshift の統合が発表されました！（プレビュー） #reinvent	https://dev.classmethod.jp/articles/amazon-aurora-zero-etl-integration-with-amazon-redshift/	amazonredshif	2022-11-29 17:44:10
技術ブログ	Developers.IO	[新機能] Amazon RedShift integration for Apache Spark が発表されました #reinvent	https://dev.classmethod.jp/articles/redshift-ntegration-for-spark-reinvent22/	tintegrationforapachespar	2022-11-29 17:20:29
技術ブログ	Developers.IO	[レポート]SageMakerでのHugging Faceの活用について学ぶ #BOA304 #reinvent	https://dev.classmethod.jp/articles/reinvent2022-report-boa304/	boareinvent	2022-11-29 17:06:43
技術ブログ	Developers.IO	【速報】OpenSearchをサーバーレスで実行できるAmazon OpenSearch ServerlessがPreviewで発表されました！ #reinvent	https://dev.classmethod.jp/articles/aws-reinvent-2022-opensearch-serverless/	adamselipsky	2022-11-29 17:06:33
技術ブログ	Developers.IO	[レポート] DeepRacerのWildcard レースに挑戦してみた #reinvent	https://dev.classmethod.jp/articles/deepracer-wildcard-race-reinvent2022/	deepracer	2022-11-29 17:00:36
海外TECH	Ars Technica	Musk faces fines if Twitter’s gutted child safety team becomes overwhelmed	https://arstechnica.com/?p=1900596	child	2022-11-29 17:40:41
海外TECH	Ars Technica	Musk asks if Apple “hates free speech” because it cut Twitter ad spending	https://arstechnica.com/?p=1900580	twitter	2022-11-29 17:10:41
海外TECH	Ars Technica	Bringing horrible space monsters to life with performance capture tech	https://arstechnica.com/?p=1900153	callisto	2022-11-29 17:05:55
海外TECH	MakeUseOf	How to Add a Portable Software Menu to Windows 10 & 11	https://www.makeuseof.com/windows-add-portable-software-menu/	windows	2022-11-29 17:15:15
海外TECH	DEV Community	Kubernetes Labels: Expert Guide with 10 Best Practices	https://dev.to/castai/kubernetes-labels-expert-guide-with-10-best-practices-3l03	Kubernetes Labels Expert Guide with Best PracticesWith Kubernetes labels DevOps teams can troubleshoot issues faster apply configuration changes en masse and respond quickly to issues Labels also give crucial insights into your costs boosting your monitoring allocation and management capabilities Following best practices when using labels helps you realize tremendous benefits from infrastructure visibility and efficient operations  Here s everything you need to know about Kubernetes labels what they are how they work when to use them and the best practices to follow to build a solid labeling strategy What are Kubernetes labels Kubernetes labels are key value string pairs that link identifying metadata to Kubernetes objects Kubernetes provides teams with integrated support for using labels to retrieve and filter the data from the Kubernetes API and carry out bulk operations on the selected objects Many teams use Kubernetes labels to provide DevOps with information about the ownership of a node a pod or other Kubernetes objects for easier tracking and operational decision making When creating a new label you must comply with the restrictions Kubernetes places on the length and allowed values A label value must contain characters or less a label s value can also be empty start and end with an alphanumeric character unless it s empty only include dashes underscores dots and alphanumerics You can find the labels a Kubernetes object has by using kubectl For example to get all labels for a pod named pod you can run gt kubectl get pod o json jq metadata labelsTo create a label you can specify them in your configuration file spec s metadata labels object Let s consider the pod yaml file that describes a single pod apiVersion vkind Podmetadata  name nginx labels    environment dev   critical true spec  containers     image nginx     name nginx     resources        requests          cpu mNote that the value of the critical label is “true and not true That is because labels as well as their values must be strings Let s apply the configuration file gt kubectl apply f pod yamlpod nginx createdYou can now apply or overwrite a label directly on an already existing Kubernetes object using kubectl First get all the labels that the pod has gt kubectl get pod nginx o json jq metadata labels    critical true    environment dev Now to change the environment label s value and add a new key value label pair deprecated true we execute the following command gt kubectl label pod nginx environment prod overwritepod nginx labeled gt kubectl label pod nginx deprecated truepod nginx labeledKeep in mind that updating a label s value is not allowed unless you explicitly overwrite it with the overwrite flag The resulting labels are as follows gt kubectl get pod nginx o json jq metadata labels    deprecated true    critical true    environment prod Kubernetes labels vs annotationsKubernetes offers two tactics for connecting metadata with objects labels and annotations Annotations are key value pairs that connect non identifying metadata with objects For instance an annotation could contain logging or monitoring information for a given resource The main difference between labels and annotations is that annotations are not used to filter group or operate over the Kubernetes resource Instead you can use them to access additional information about it For example the annotations for the node where the previously deployed pod has been scheduled are as follows gt kubectl get node demo node o json jq metadata annotations    kubeadm alpha kubernetes io cri socket unix var run cri dockerd sock    node alpha kubernetes io ttl    volumes kubernetes io controller managed attach detach true Those annotations do not provide any information about the node s characteristics Instead they offer some data on how the node works When to use Kubernetes labels Group resources for object queriesIf you add the same label key value pair to multiple resources other people can easily query for all of them For example a DevOps engineer discovers that a development environment is unavailable At this point they can quickly check the status of all pods including the label environment dev Here s an example command gt kubectl get pods l environment dev NAME READY STATUS RESTARTS AGEnginx CrashLoopBackOff mThis lets the team instantly see the affected pods and resolve the issue much faster than going through all the resources and picking just the ones in the dev environment In a complex case with many different deployments finding the right dev pods would take the DevOps engineer ages if the engineering team didn t add the environment dev label to the resources The DevOps engineer would have to use a generic kubectl get pods command and then comb through the output using a tool like grep Perform bulk operationsAnother use case of Kubernetes labeling is to carry out bulk operations based on the resource labels  Suppose that an engineer removes all staging environments every night to reduce cloud costs By using Kubernetes labels they can easily automate this task  For instance here s a command that deletes all objects labeled environment local environment dev or environment staging gt kubectl delete deployment services statefulsets l environment in local dev staging Schedule pods based on node labelsThe hidden gem of Kubernetes labels is that they are heavily used in Kubernetes itself for scheduling pods to appropriate nodes By using labels you can have more control over the resources you create by making Kubernetes schedule specific deployments onto specific nodes Let s see how this works in practice gt kubectl get nodesNAME STATUS ROLES AGE VERSIONgke node fe Ready lt none gt d v gke gke node cdfdb Ready lt none gt d v gke gke node fbcf Ready lt none gt d v gke gt kubectl get nodes l critical true No resources foundCurrently no nodes with the label critical true exist  Let s try to create a pod that has to be scheduled on a node with the label critical true using a node selector Here is a pod yaml configuration file for that apiVersion vkind Podmetadata name nginx labels environment prodspec nodeSelector critical true containers image nginx name nginx resources requests cpu mNow let s apply it and check what happens gt kubectl apply f pod yamlpod nginx created gt kubectl get pod nginxNAME READY STATUS RESTARTS AGEnginx Pending m gt kubectl get events field selector involvedObject name nginxLAST SEEN TYPE REASON OBJECT MESSAGEs Warning FailedScheduling pod nginx nodes are available node s didn t match Pod s node affinity selector preemption nodes are available Preemption is not helpful for scheduling Note that the pod cannot get scheduled on any of the nodes since none of them has the required label  Now let s label one of the nodes with the required label gt kubectl label node gke node fbcf critical truenode gke node fbcf labeled gt kubectl get nodes l critical true NAME STATUS ROLES AGE VERSIONgke node fbcf Ready lt none gt h v gke And now let s check the pod gt kubectl get pod nginxNAME READY STATUS RESTARTS AGEnginx Running msThe pod has been successfully scheduled to the node Keep in mind that if multiple labels are specified in the node selector they all must be satisfied by a node in order for the pod to get scheduled on it best practices for Kubernetes labels Make use of the labels recommended by KubernetesKubernetes provides a list of recommended labels for grouping objects For example Kubernetes recommends using app kubernetes io name and app kubernetes io instance to represent the application s name and instance respectively Just drop the prefix “app kubernetes io and add your company s subdomain to customize the labels   Pay attention to correct syntaxTo create a Kubernetes label key value pair you need to use the following syntax lt prefix gt lt name gt Let s dive into the details lt prefix gt The prefix is optional if you choose to use it it needs to be a valid DNS subdomain such as cast ai and have no more than characters in total Prefixes come in handy for tools and commands that aren t private to users They are also helpful because they let teams use multiple labels that would otherwise conflict think of the ones in third party packages Note that the kubernetes io and ks io prefixes are reserved for Kubernetes core components lt name gt This part refers to the arbitrary property name of the label Teams can use the name “environment with label values such as “production or “testing for clarity  A name must meet the same requirements as the label value but it can t be empty Hence the name needs to have characters or less beginning and ending with an alphanumeric character a z A Z with dashes underscores dots and alphanumerics in between Standartize label naming conventionsMultiple teams using Kubernetes need to follow the same labeling conventions Otherwise all the labeling effort will bring you no value  It s a good practice to have your development pipeline carry out static code analysis on resource configuration files to ensure that all the required labels are there If you fail to apply labels properly automated processes may get broken and any monitoring solutions you use may send you false positive alerts Avoid unnecessary changes to labelsLabels in Kubernetes are used to identify and select resources for scheduling deployment and administration purposes As a result modifying a resource s label can have far reaching and unforeseen implications  For instance if you switch a group of pods app label from frontend to backend Kubernetes can reschedule those pods onto nodes that aren t set up to run the backend app The pods can crash as a result making them unavailable It s crucial only to modify labels when it is absolutely essential and carefully evaluate the ramifications of any changes before making them to avoid these kinds of issues Use label selection optionsTeams can select labeled objects based on equality and sets Selections based on equality allow you to retrieve objects with labels equal or not equal to the specified value or values Diving down into syntax and both represent equality while represents inequality It s possible to add multiple labels separated by commas all conditions need to match here For example if you execute the following command gt kubectl get pods l environment dev release daily it will return all the pods that have labels environment dev AND release daily On the other hand selections based on sets allow finding resources with multiple values at once Sets are similar to the IN keyword in SQL For example the following command gt kubectl get pods l environment in prod dev will find all the pods that contain the label environment prod OR environment dev Don t store application level semantics in labelsKubernetes labels may come together with an object s metadata but they re not supposed to serve as a data store for applications Given that Kubernetes resources are often used for a short period of time and aren t tightly associated with applications labels quickly become unsynchronized and therefore useless Don t store sensitive information in labelsIf someone gains access to your Kubernetes cluster while you store passwords or API credentials or other sensitive data in labels they will be able to see it in plain text This is a significant security risk and may have negative effects like identity theft or data breaches It is advisable to preserve sensitive information in secrets rather than labels Secrets are encrypted and only the pods that require them may decrypt them By doing this even if someone manages to access your Kubernetes cluster they won t be able to view the private data kept in secrets Add labels to pod templatesAdd essential labels to pod templates that are part of workload resources That way Kubernetes controllers can consistently create pods with the states you ve specified  The goal should not be to create as many labels as possible but to create labels that bring value to your team Start small and create a list of labels to be part of the template For example you can start by identifying the resource owners the environment the resource is running in and the release Automate your labeling practiceAutomation can save you plenty of time and labeling is no exception to that If you have a continuous integration continuous delivery CI CD pipeline set up you can easily automate some labels for cross cutting concerns  It s smart to automatically attach labels with CD tooling since it guarantees consistency and makes engineers more productive It s also a good practice to have CI jobs enforce proper labeling by making a build fail and sending a notification to the responsible team if a label is missing   Use labels for cost monitoringLabels are very helpful for gaining a better understanding of your Kubernetes cloud costs Cost monitoring allocation and management all rely on a proper labeling strategy  If multiple tenants share resources in a single cluster you need to use relevant labels to create a cost allocation report This is how you can determine which team service or application generated specific costs which helps greatly when investigating an unexpected cost spike Use this free monitoring tool to track your costs by labelsCAST AI provides a cost monitoring tool that allows you to stay updated on the costs of any of your workloads The costs can be filtered by any label that exists on any of your workloads making it easy to track cloud costs per team service or any other label that you use The option to group workloads by label is coming soon  See the difference good labeling and cost monitoring can make by connecting your cluster to CAST AI s free cost monitoring solution	2022-11-29 17:21:14
Apple	AppleInsider - Frontpage News	Kensington's new SlimBlade Pro Trackball is ergonomic & ambidextrous	https://appleinsider.com/articles/22/11/29/kensingtons-new-slimblade-pro-trackball-is-ergonomic-ambidextrous?utm_medium=rss	Kensington x s new SlimBlade Pro Trackball is ergonomic amp ambidextrousKensington s new SlimBlade Pro Trackball provides users with an ergonomic way to navigate their Mac or another computer Kensington SlimBlade Pro TrackballThe plug and play SlimBlade Pro offers connectivity through Bluetooth GHz wireless or a wired option It has a rechargeable battery that offers up to four months of usage per charge ーand it charges with USB C Read more	2022-11-29 17:53:53
Apple	AppleInsider - Frontpage News	How to cut off your ex from your Netflix account	https://appleinsider.com/inside/mac/tips/how-to-cut-off-your-ex-from-your-netflix-account?utm_medium=rss	How to cut off your ex from your Netflix accountYou re never ever getting back together with your ex Here s how to cut off your ex or anyone else from your Netflix account with a brand new feature Credit David Balev UnsplashOn November Netflix announced a new feature called Managing Access and Devices This allows subscribers who are paying for the account to remotely control who can watch content using their Netflix account Read more	2022-11-29 17:49:28
Cisco	Cisco Blog	Defeating Complexity with Cisco Enterprise Networking Innovations	https://blogs.cisco.com/networking/defeating-complexity-with-cisco-enterprise-networking-innovations	Defeating Complexity with Cisco Enterprise Networking InnovationsCisco s mission is to help you transform your infrastructure to meet demands and provide a unified experience for your employees regardless of where they are In our latest Cisco SD WAN release we have taken a large step forward in supporting our efforts to simplify user experiences everywhere	2022-11-29 17:00:54
海外科学	NYT > Science	China Launches Astronauts to Tiangong Space Station: Video and Updates	https://www.nytimes.com/2022/11/29/world/asia/china-space-launch-astronauts.html	China Launches Astronauts to Tiangong Space Station Video and UpdatesAfter decades of military secrecy Chinese officials opened their desert rocket launch center to a handful of visitors and called for international cooperation in space	2022-11-29 17:16:12
海外科学	NYT > Science	Can This Man Stop Lying?	https://www.nytimes.com/2022/11/29/health/lying-mental-illness.html	illness	2022-11-29 17:36:21
海外科学	BBC News - Science & Environment	Bird flu: Free range turkey supplies hit by bird flu	https://www.bbc.co.uk/news/science-environment-63797896?at_medium=RSS&at_campaign=KARANGA	avian	2022-11-29 17:53:29
ニュース	BBC News - Home	Channel migrants: Man arrested in UK over 27 dinghy deaths	https://www.bbc.co.uk/news/uk-63798798?at_medium=RSS&at_campaign=KARANGA	gloucestershire	2022-11-29 17:43:34
ニュース	BBC News - Home	Ukraine war: Nato pledges to provide more weapons and fix power grid	https://www.bbc.co.uk/news/world-europe-63798506?at_medium=RSS&at_campaign=KARANGA	russian	2022-11-29 17:32:28
ニュース	BBC News - Home	Bird flu: Free range turkey supplies hit by bird flu	https://www.bbc.co.uk/news/science-environment-63797896?at_medium=RSS&at_campaign=KARANGA	avian	2022-11-29 17:53:29
ニュース	BBC News - Home	Driving examiners across UK to stage strikes over pay	https://www.bbc.co.uk/news/business-63800593?at_medium=RSS&at_campaign=KARANGA	january	2022-11-29 17:53:12
ニュース	BBC News - Home	Can age verification stop children seeing pornography?	https://www.bbc.co.uk/news/technology-63794796?at_medium=RSS&at_campaign=KARANGA	check	2022-11-29 17:13:09
ニュース	BBC News - Home	World Cup 2022: Ecuador 1-2 Senegal - Ismaila Sarr & Kalidou Koulibaly put Africans into last 16	https://www.bbc.co.uk/sport/football/63711991?at_medium=RSS&at_campaign=KARANGA	World Cup Ecuador Senegal Ismaila Sarr amp Kalidou Koulibaly put Africans into last Kalidou Koulibaly s volley sends Senegal into the World Cup s knockout stages as they eliminate Ecuador at a rowdy Khalifa Stadium	2022-11-29 17:53:52
ニュース	BBC News - Home	World Cup 2022: Netherlands 2-0 Qatar: Gakpo and De Jong score to ensure top spot	https://www.bbc.co.uk/sport/football/63711993?at_medium=RSS&at_campaign=KARANGA	World Cup Netherlands Qatar Gakpo and De Jong score to ensure top spotIn form Cody Gakpo scores again as the Netherlands finish top of Group A ending Qatar s miserable involvement at their home World Cup with a third consecutive defeat	2022-11-29 17:04:39
ビジネス	ダイヤモンド・オンライン - 新着記事	「勉強は習慣が9割」と断言できる意外な理由 - 逆転合格90日プログラム	https://diamond.jp/articles/-/313680	逆転	2022-11-30 02:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	認知症になる前に絶対にやっておきたい脳トレ - 1分間瞬読ドリル　超かんたん!入門編	https://diamond.jp/articles/-/313693	認知症になる前に絶対にやっておきたい脳トレ分間瞬読ドリル超かんたん入門編「認知症、ボケ予防に役立つ」「記憶力や思考力がアップし、勉強に活かせる」「頭の回転が速くなった」「本が速く読めて、判断スピードがあがった」「モチベーションの向上、習慣化につながる」「持続力が増して途中で投げ出さなくなった」などの声が届いた、くり返し楽しんで使える『分間瞬読ドリル』に、超入門編が登場。	2022-11-30 02:50:00
北海道	北海道新聞	中国、３５年に核弾頭１５００発　台湾侵攻警戒、米国防総省報告書	https://www.hokkaido-np.co.jp/article/767494/	国防総省	2022-11-30 02:11:00
GCP	Cloud Blog	Boost medical discoveries with AlphaFold on Vertex AI	https://cloud.google.com/blog/topics/developers-practitioners/boost-medical-discoveries-alphafold-vertex-ai/	Boost medical discoveries with AlphaFold on Vertex AIThere s a lot we can learn from combining technology with science to help support the development of amazing discoveries By using an AI system to predict protein shapes we have the potential to accelerate research in every field of biology Inside every cell in your body billions of tiny molecular machines are hard at work They are what allow your eyes to detect light your neurons to fire and the instructions in your DNA to be read These intricate machines are known as proteins The protein folding puzzleProtein folding is something that occurs naturally so that proteins become biologically functional but it s a complex process that sometimes fails For decades scientists have been trying to find a method to reliably predict a protein s structure from its sequence of amino acids so we can better understand how proteins work The challenge There are over million known distinct proteins Each one has a unique D shape that determines how it works and what it does Because there are so many sequences and determining their D structure experimentally is so time consuming and expensive scientists only know the exact structure of a tiny fraction of the proteins And these experimental methods still fall far short of reliable statistical accuracy Deepmind s gigantic leapIn  Alphabet s artificial intelligence research arm DeepMind made a massive breakthrough in predicting protein structures using a deep learning model called AlphaFold AlphaFold is trained on publicly available data consisting of about protein structures and is the first computational method that can regularly predict the D shape of a protein at scale with a high degree of accuracy  AlphaFold has already sent waves throughout the scientific community and has demonstrated the potential for AI to aid fundamental scientific discovery Recently Deepmind has made AlphaFold predictions available and open source to anyone To date more than researchers from countries have accessed the AlphaFold protein structure database to get closer to finding life saving cures for diseases like Leishmaniasis and Chagas And now Deepmind has expanded the set of available predictions by more than times from nearly million to nearly million to cover almost all cataloged proteins found in nature  Open source predictions available on Google CloudTogether Google Cloud and Deepmind have released this dataset of predicted protein structures for plants bacteria animals and other organisms as part of the Google Cloud Public Dataset program to enable bulk downloads at no cost That means you can also create custom queries of the dataset using BigQuery Running AlphaFold on Google Cloud Vertex AILet s say you want to run AlphaFold on your own in order to get protein structure predictions against your own set of data There are a few challenges to keep in mind You need to set up feature engineering against genetic sequence databasesPreprocess dataAnd run those inputs against pre trained modelsAll of this requires allocating CPUs or GPUs hosting a notebook environment and scaling up for larger experiments It s hard to build and configure an on premise system or cloud server to use AlphaFold whether you just want to try it out or run it at scale as a large organization That s why we re excited to share a deep integration between Google Cloud and Deepmind On top of the Public Datasets program we have created end to end code samples for AlphaFold on Vertex AI a managed end to end ML platform to help address these challenges and speed up deployment With AlphaFold on Vertex AI you can manage a data science or machine learning workflow in a single development environment You get access to pre configured compute storage and end to end production notebooks We have removed the heavy lifting needed to set up new ML environments automate orchestration and manage large clusters The AlphaFold inference workflow can be simplified with Vertex AI from data preparation to feature engineering and deployment Unlike the manual set up the orchestrator makes it possible to parallelize steps get predictions faster and with better tracking Try it out first using Vertex AI WorkbenchFor those of you who want to try out a simplified version of AlphaFold we have a Colab notebook that uses no templates homologous structures and a selected portion of the BFD database You can deploy right on Vertex AI Workbench which lets you specify a custom container image that we ve already created for you You ll be able to Configure access to genetic databasesConfigure GPU accelerationSearch against genetic databasesUse the pre processed results as inputs to the AlphaFold model locally In a little over an hour you can harness the power of AlphaFold to generate D protein structures from amino acid sequences  Run hundreds of experiments reliably using Vertex AI PipelinesFor organizations that want to run a full blown version of AlphaFold for many protein folding experiments a week you ll want an ML pipeline orchestrator The AlphaFold Batch Inference solution is a set of code samples that uses Vertex AI Pipelines to support hundreds of concurrent inference pipelines with higher throughput to help you run experiments at scale The solution uses Vertex AI Pipelines as an orchestrator and runtime Vertex ML Metadata for metadata and artifacts and Cloud Filestore to manage databases Click to enlargeBecause it s built on Vertex AI Pipelines you can automate monitor and experiment with interdependent parts of an ML workflow The minimized inference elapsed times mean what normally would take you days can now take you hours The solution includes two example pipelines The universal pipeline solution mirrors the exact logic in DeepMind s open source inference script but decoupled into discrete tasks so you can run the same experiments faster more efficiently and with better tracking The customized pipeline solution shows you how to further optimize the inference workflow by parallelizing feature engineering steps so you can plug in your own database sources  You get example components pipelines and notebooks to start analyze and recompile pipelines on different GPUs The AlphaFold Vertex AI Workbench solution is great for experimental use while the AlphaFold Batch Inference solution on Vertex AI Pipelines is great for doing protein folding at scale with a strong process for reproducibility and tracking Now go forth and save the world Okay maybe that s a bit hyperbolic but this is inspiring stuff What started as a year challenge to the discovery of AlphaFold to being able to run it on Google Cloud researchers developers and science enthusiasts now have access to one of the most pivotal advancements in the medical world Even a non specialist can easily use a Vertex AI notebook to exercise a simplified version of AlphaFold The next answers to the mysteries of life and discovery of disease treatments have never felt more attainable With these no cost solutions to run AlphaFold on Vertex AI and the Public Dataset you can help propel us in this worldwide endeavor  Learn more about healthcare and life sciences solutions on Google Cloud here  If you have feedback or want to share your experience with me reach out to me at stephr wong Related ArticleBio pharma organizations can now leverage the groundbreaking protein folding system AlphaFold with Vertex AIHow to run DeepMind s AlphaFold on Google Cloud s Vertex AI Read ArticleRelated ArticleRunning AlphaFold batch inference with Vertex AI PipelinesCode samples and guidelines demonstrating how you can effectively implement the AlphaFold workflow on Vertex AI Read Article	2022-11-29 18:00:00
GCP	Cloud Blog	Seer Interactive gets the best marketing results for their clients using Looker	https://cloud.google.com/blog/products/data-analytics/seer-interactive-builds-data-analytics-platform-with-looker/	Seer Interactive gets the best marketing results for their clients using LookerMarketing strategies based on complex and dynamic data get results However it s no small task to extract easy to act on insights from increasing volumes and ever evolving sources of data including search engines social media platforms third party services and internal systems That s why organizations turn to us at Seer Interactive We provide every client with differentiating analysis and analytics SEO paid media and other channels and services that are based on fresh and reliable data not stale data or just hunches  More data more waysAs digital commerce and footprints have become foundational for success over the past five years we ve experienced exponential growth in clientele Keeping up with the unique analytics requirements of each client has required a fair amount of IT agility on our part After outgrowing spreadsheets as our core BI tool we adopted a well known data visualization app only to find that it couldn t scale with our growth and increasingly complex requirements either We needed a solution that would allow us to pull hundreds of millions of data signals into one centralized system to give our clients as much strategic information as possible while increasing our efficiency After outlining our short and long term solution goals we weighed the trade offs of different designs It was clear that the data replication required by our existing BI solution design was unsustainable  Previously all our customer facing teams created their own insights More than consultants were spending hours each week pulling and compiling data for our clients and then creating their own custom reports and dashboards As data sets grew larger and larger our desktop solutions simply didn t have the processing power required to keep up and we had to invest significant money in training any new employees in these complex BI processes Our ability to best serve our customers was being jeopardized because we were having trouble serving basic needs let alone advanced use cases We selected Looker Google Cloud s business intelligence solution as our BI platform As the direct query leader Looker gives us the best available capabilities for real time analytics and time to value Instead of lifting and shifting we designed a new consolidated data analytics foundation with Looker that uses our existing BigQuery platform which can scale with any amount and type of data We then identified and tackled quick win use cases that delivered immediate business value for our team and clients   Meet users where they are in skills requirements and preferencesOne of our first Looker projects involved redesigning our BI workflows We built dashboards in Looker that automatically serve up the data our employees need along with filters they use to customize insights and set up custom alerts Users can now explore information on their own to answer new questions knowing insights are reliable because they re based on consistent data and definitions More technical staff create ad hoc insights with governed datasets in BigQuery and use their preferred visualization tools like Looker Studio Power BI and Tableau We ve also duplicated some of our data lakes to give teams a sandbox that they can experiment in using Looker embedded analytics This enables them to quickly see more data and uncover new opportunities that provide value to our clients Our product development team is also able to build and test prototypes more quickly letting us validate hypotheses for a subsection of clients before making them available across the company And because Looker is cloud based all our users can analyze as much data as they want without exceeding the computing power of their laptops Seamless security and faster developmentWe leverage BigQuery s access and permissioning capabilities Looker can inherit data permissions directly from BigQuery and multiple third party CRMs so we ve also been able to add granular governance strategies within our Looker user groups This powerful combination ensures that data is accessed only by users who have the right permissions And Looker s unique “in database architecture means that we aren t replicating and storing any data on local devices which reduces both our time and costs spent on data management while bolstering our security posture  Better services and hundreds of thousands of dollars in savingsTime spent on repetitive tasks adds up over months and years With Looker we automate reports and alerts that people frequently create Not only does this free up teams to discover insights that they previously wouldn t have time to pinpoint but they have fresh reports whenever they are needed For instance we automated the creation of multiple internal dashboards and external client analyses that utilize cross channel data In the past before we had automation capabilities we used to only generate these analyses up to four times a year With Looker we can scale and automate refreshed analyses instantlyーand we can add alerts that flag trends as they emerge We also use Looker dashboards and alerts to improve project management by identifying external issues such as teams who are nearing their allocated client budgets too quickly or internal retention concerns like employees who aren t taking enough vacation time Using back of the napkin math let s say every week different people spend at least one hour looking up how team members are tracking their time By building a dashboard that provides time tracking insights at a glance we save our collective team hours a year And if we assume the hourly billable rate is an hour we re talking in savingsーjust from one dashboard Drew Meyer Director of Product Seer InteractiveThe insights and new offerings to stay ahead of trends Looker enables us to deliver better experiences for our team members and clients that weren t possible even two years ago including faster development of analytics that improve our services and processes For example when off the shelf tools could not deliver the keyword tracking insights and controls we required to deliver differentiating SEO strategies for clients we created our own keyword rank tracking application using Looker embedded analytics Our application provides deep dive SEO data exploration capabilities and gives teams unique flexibility in analyzing data while ensuring accurate consistent insights Going forward we ll continue adding new insights data sources and automations with Looker to create even better informed marketing strategies that fuel our clients success	2022-11-29 17:30:00