投稿時間:2023-02-03 02:21:27 RSSフィード2023-02-03 02:00 分まとめ(29件)

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IT	気になる、記になる…	Apple、｢iOS 16.3.1｣のアップデートを準備中	https://taisy0.com/2023/02/03/168029.html	apple	2023-02-02 16:16:52
AWS	AWS Japan Blog	Amazon OpenSearch Serviceにおけるカスタムパッケージと辞書ファイルのホットリロード	https://aws.amazon.com/jp/blogs/news/custom-packages-and-hot-reload-of-dictionary-files-with-amazon-opensearch-service/	hotreloadofdictionaryfile	2023-02-02 16:51:15
AWS	AWS - Webinar Channel	Building a Modern Data Platform on Amazon EKS - AWS Online Tech Talk	https://www.youtube.com/watch?v=7AHuMNqbR7o	Building a Modern Data Platform on Amazon EKS AWS Online Tech TalkKubernetes has dramatically risen in popularity around the world since its inception as an open source CNCF project in While it was originally designed for stateless applications more users than ever before are building their next generation data platforms using services like Amazon Elastic Kubernetes Service EKS Kubernetes is powering a variety of use cases including batch processing and normalizing data sets data ingestion with streaming capabilities and producing data insights with Machine Learning to create targeted and personalized promotional campaigns This tech talk will explore several advantages and challenges of building data platforms on Kubernetes and introduce Data on EKS a global initiative offering IaC patterns in Terraform and CDK benchmark reports best practices and sample code to simplify and accelerate the journey for users who want to run applications like Spark Kafka Kubeflow Ray Airflow Presto and Cassandra on EKS Learning Objectives Objective Gain access to useful tools that will help accelerate your data journey on Amazon EKS Objective Learn about key tradeoffs and considerations when modernizing data workloads on Kubernetes Objective Understand best practices techniques and integrations to help modernize Data Platform for optimal cost reliability and performance To learn more about the services featured in this talk please visit To download a copy of the slide deck from this webinar visit	2023-02-02 16:22:29
python	Pythonタグが付けられた新着投稿 - Qiita	【Python】igraphでグラフ・ネットワークを生成・描画する	https://qiita.com/h-nabata/items/a17b41fd4e72b2855e8a	igraph	2023-02-03 01:25:36
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Rails】render.comを使ってデプロイする	https://qiita.com/takuyannnnn19/items/d7a52368206d7e9a1b16	osmacosm	2023-02-03 01:25:27
AWS	AWSタグが付けられた新着投稿 - Qiita	【メモ】AWS SAA-C03におけるメモ	https://qiita.com/railgun-0402/items/79c7cf6b7b9edf9cf6e3	transferaccele	2023-02-03 01:18:59
AWS	AWSタグが付けられた新着投稿 - Qiita	Terragruntってのがあるらしい	https://qiita.com/m9e/items/d7f51308cf08510b672b	github	2023-02-03 01:13:55
Git	Gitタグが付けられた新着投稿 - Qiita	【Git】push時に生じる error:refspec src ... does not match any とは何なのか？	https://qiita.com/rtake/items/e9b8a45efbdc8b997e07	branchgtltremotebranchgt	2023-02-03 01:17:40
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】render.comを使ってデプロイする	https://qiita.com/takuyannnnn19/items/d7a52368206d7e9a1b16	osmacosm	2023-02-03 01:25:27
海外TECH	Ars Technica	PS5 owners won’t get this set of free PS4 games for much longer	https://arstechnica.com/?p=1914436	downloads	2023-02-02 16:18:02
海外TECH	MakeUseOf	8 Common Apple Music Issues and How to Fix Them	https://www.makeuseof.com/common-apple-music-issues-how-to-fix/	common	2023-02-02 16:30:16
海外TECH	MakeUseOf	6 Different Ways to Open Programs on Windows	https://www.makeuseof.com/open-apps-programs-windows/	windows	2023-02-02 16:15:15
海外TECH	DEV Community	HarperDB: Staking a Claim as the Only Globally Distributed Edge Data Platform	https://dev.to/harperdb/harperdb-staking-a-claim-as-the-only-globally-distributed-edge-data-platform-44a2	HarperDB Staking a Claim as the Only Globally Distributed Edge Data PlatformLike most companies in general and especially tech startups HarperDB has overcome many challenges to get to where we are today which is the most exciting place we ve been as a company thus far We ve learned that we were a couple years too early to market and that being too early can surprisingly be more difficult than being too late Over the years we have explored different solutions and industries to find the right product market fit which has been a major roadblock We learned from and built upon these experiences for example if we hadn t ventured into IIoT HarperDB would not be what it is today in terms of scale and distributed capability Due to perseverance we have now positioned HarperDB as the only Globally Distributed Edge Data Platform providing unprecedented value to our customers through avenues such as Edge Computing AI ML APIs and third party integrations Considered a lean startup HarperDB has achieved much more than competitors with much less funding under our belt With K total HarperDB deployments and K community members subscribers our team continues to expand the product to meet the dynamic needs of our innovative customers was HarperDB s highest year in sales yet and we expanded our enterprise partnerships and customer base now working with companies like Lumen Akamai Google Verizon and Equinix to bring an end to end solution to the market More Reason to CelebrateHarperDB is thrilled to be announced as one of Built in Colorado s Best Places to Work ranked at number two on the Best Startups list Staying true to our core values as a company our leadership has ensured that this continues to be a safe fair and enjoyable place to work even through stages of rapid growth HarperDB s CEO Stephen Goldberg was just named the Bill Daniels Ethical Leader of the Year Now in its eighth year the award recognizes local leaders that demonstrate remarkable integrity and ethics in business exemplifying the ethical principles practiced by cable television pioneer Bill Daniels We just released HarperDB which integrates with open source connectivity platform NATS to enable limitless data distribution Learn more in this SiliconAngle article As we wrap up and gain momentum for I sat down with HarperDB s CEO amp Co Founder Stephen Goldberg for a brief Q amp A What are HarperDB s current strategic initiatives We started the company with the idea of trying to make the easiest database in the world that is also massively scalable As we evolve we still want to solve that but we can also provide a lot of value driving low latency use cases because HarperDB is uniquely positioned as a fully distributed platform that allows for quickly distributing data globally anywhere on earth So for now we are focused on distributed capabilities as well as performance Looking ahead we will be focused on improving HarperDB as a platform The product is extremely performant easy to use and highly distributable as is but it could still use work as a platform in making developers lives easier so that they can have a global application platform in one place This ties into edge computing because right now you can have HarperDB anywhere in the world but in the future we want that to be an entirely seamless process so that you can deploy globally with the click of a button Why is HarperDB focused on Edge Computing and how is the edge helping organizations that we work with Originally we were focused on IIoT and IoT which is actually different from edge computing IoT is really about the things devices etc in the field While HarperDB has the ability to continue playing a role in that where we can add the most value is at the edge a step back from IoT We are taking what is also the cloud and making it more local closer to the end user and much more distributed We accidentally built the most distributed database in the world that is uniquely horizontally scalable so we found that our technology is really meaningful in these use cases where it can be deployed anywhere Telecom companies for example have noticed that HarperDB can be deployed anywhere in their network and solve a lot of problems around edge computing Interestingly people think that G makes edge computing easier but in a lot of ways it can make it more difficult A lot of companies are dealing with major congestion issues due to different pipes of data going in and out from the cloud and now to and from the edge This complex congestion in the data pipeline causes high latency and high cost Because HarperDB can make your data available at the edge edge computing can become real You can build your entire application on HarperDB so now you really have everything you need along with the ability to use G and other globally distributed infrastructure now available What projects are you most excited about right now We ve talked a lot about one of our customers named Edison Interactive They were struggling with high latency and lack of real time decision making and we were able to simplify the transition to a distributed architecture ultimately reducing latency from seconds down to milliseconds or less You can learn more about this case study on our website or on AWS s blog We re currently exploring similar use cases in industries like gaming media manufacturing oil and gas etc Some of this we can t quite talk about yet but big things are happening Overall I m not as excited about one specific area as much as being excited about the convergence of all these things and all these ideas that we ve been talking about for a while finally becoming real What are some of the adoption challenges that enterprises face with Edge Computing There are some challenges around organizations taking the patterns that they re comfortable with in the cloud and applying that to the edge Security is obviously one that can also become interesting It s really important to understand that the edge while similar to cloud can be quite different The biggest challenge with implementing an edge strategy is the interoperability of the different partnerships that you need in place AWS made cloud so easy because they have everything you need consolidated into one package At the edge there is no dominant player enabling you to put your whole stack in one place so organizations must put different solutions and partnerships together to achieve their end goal With HarperDB one large benefit is that we can run anywhere on anything and it doesn t matter who s above or below us you get the same experience anywhere Is there space for AI and Machine Learning in Edge Computing How does it all tie together We re really excited about AI and machine learning ML on the edge We re already working on some projects in this space Kevin from our team built a machine learning use case with HarperDB using TensorFlow for a customer Machine learning is awesome for understanding your data after the fact in the cloud but at the edge you can do real time ML and personalized content experiences and personalized interactions ML and AI can require huge amounts of compute and huge amounts of data but if you distribute that you can do really exciting things such as having human interaction like levels of data at the edge By distributing to the edge you reduce the barrier to entry reduce latency and cost etc How is HarperDB partnering with other organizations to make Edge Computing real We re partnered with Verizon we worked with them on the Edison use case Companies like Akamai are helping us distribute our technology globally HarperDB is also partnering with a lot of cloud providers to run on their edge offerings with Wavelength and MEC Multi Access Edge Computing These partnerships are exciting because to deliver a globally distributed platform it takes more than just one piece of the puzzle so these companies allow us to deliver a robust solution to our customers Predictions related to Edge Cloud and IoT moving forward in the next years All of these siloed areas are moving towards a more powerful convergence Machine learning and AI is one discipline you also have data science G and different edge offerings Then there s a different sector of edge computing without G which is much more distributed using existing technologies like containerization Kubernetes and other fabric level technologies to create a highly distributed edge You also have blockchain and so on So we have this fairly fragmented set of disciplines which are beginning to converge and overlap It s too siloed right now but there is a gravitational pull that s moving them towards each other Once these disciplines become more overlapped we ll begin to see more incredible use cases coming to fruition and the future becomes real You have robotics real world human interaction with true AI not just ML and personalized interaction For example imagine walking into a store or hotel room and the entire experience is tailored to you autonomous vehicles etc…All of these different moving parts coming together will make these ambitions real and it s exciting to say the least	2023-02-02 16:41:21
Apple	AppleInsider - Frontpage News	Apple says popular demand brought back the HomePod	https://appleinsider.com/articles/23/02/02/apple-says-popular-demand-brought-back-the-homepod?utm_medium=rss	Apple says popular demand brought back the HomePodApple executives say that growing interest in a larger HomePod caused them to revisit the decision to discontinue the original model The new HomePod is priced at When Apple discontinued its first HomePod in March the company said that it would be focusing instead on the lower cost HomePod mini Then following rumors of its return a new and slightly revised HomePod was announced in January with availability from February Read more	2023-02-02 16:36:53
海外TECH	Engadget	Arlo video doorbells and security cameras are up to half off	https://www.engadget.com/arlo-video-doorbell-security-camera-good-deal-164306070.html?src=rss	Arlo video doorbells and security cameras are up to half offFolks on the lookout for a new video doorbell or home security camera might be interested in checking out the latest sale on Arlo gear You can snap up the devices for up to half off at the minute with the Essential Wire Free Video Doorbell seeing the biggest drop from to The doorbell which you can plug in if you wish offers a degree wide field view and HD video with HDR It captures video prior to motion activated recordings so you can see what caught the camera s attention such as what someone was doing right before ringing the doorbell You can speak to whoever s at your door from your phone thanks to two way audio support Alternatively you can respond with quick reply prompts if you re busy Arlo says the device is durable too so it should be able to withstand the elements The doorbell should play nicely with other smart home security devices as it has Amazon Alexa Google Assistant and Samsung SmartThings support Those who take out an Arlo Secure plan meanwhile will receive notifications when the doorbell detects people vehicles and packages The sale also includes a wired version of the doorbell which is percent off at A two pack of the spotlight camera which offers p video capture and color night vision without the need for a hub will run you percent off Meanwhile an indoor camera with a privacy shield is off at Follow EngadgetDeals on Twitter and subscribe to the Engadget Deals newsletter for the latest tech deals and buying advice	2023-02-02 16:43:06
Cisco	Cisco Blog	Cisco Catalyst 9000 Core Switches: Don’t Let Your Core Stop Turning	https://blogs.cisco.com/networking/cisco-catalyst-9000-core-switches-dont-let-your-core-stop-turning	Cisco Catalyst Core Switches Don t Let Your Core Stop TurningIn this world where video collaboration is the norm and the core of your network acts as the critical conduit for nearly all the traffic Cisco is here to help your organization stay connected and productive	2023-02-02 16:00:44
海外科学	NYT > Science	Window Stickers to Prevent Bird Strikes Only Work One Way	https://www.nytimes.com/2023/02/02/climate/bird-window-strikes-stickers.html	Window Stickers to Prevent Bird Strikes Only Work One WayEvery year hundreds of millions of birds die in the United States from flying into glass New research shows how to prevent some of those deaths	2023-02-02 16:17:40
海外TECH	WIRED	'Poker Face' Is the New 'Columbo'—and That's a Good Thing for Fans	https://www.wired.com/story/poker-face-columbo-peacock/	x Poker Face x Is the New x Columbo x ーand That x s a Good Thing for FansBy mimicking a s classic Rian Johnson s new murder mystery series rewrites the streaming era s rules that everything must be bingeable	2023-02-02 16:34:09
金融	金融庁ホームページ	「脱炭素等に向けた金融機関等の取組みに関する検討会」（第４回）を開催します。	https://www.fsa.go.jp/news/r4/singi/20230202.html	金融機関	2023-02-02 17:00:00
金融	金融庁ホームページ	鈴木財務大臣兼内閣府特命担当大臣閣議後記者会見の概要（令和５年１月31日）を掲載しました。	https://www.fsa.go.jp/common/conference/minister/2023a/20230131-1.html	内閣府特命担当大臣	2023-02-02 16:15:00
ニュース	BBC News - Home	Nicola Bulley: Potential witness found in search for missing mum	https://www.bbc.co.uk/news/uk-england-lancashire-64501150?at_medium=RSS&at_campaign=KARANGA	bulley	2023-02-02 16:01:30
ニュース	BBC News - Home	UK to see shorter recession, says Bank of England	https://www.bbc.co.uk/news/business-64487179?at_medium=RSS&at_campaign=KARANGA	englandthe	2023-02-02 16:29:30
ニュース	BBC News - Home	Omagh bombing: UK government announces independent statutory inquiry	https://www.bbc.co.uk/news/uk-northern-ireland-64495873?at_medium=RSS&at_campaign=KARANGA	investigation	2023-02-02 16:21:34
ニュース	BBC News - Home	Ukraine war: 80 years on, we are facing German tanks again - Putin	https://www.bbc.co.uk/news/world-europe-64502504?at_medium=RSS&at_campaign=KARANGA	world	2023-02-02 16:42:19
ニュース	BBC News - Home	Beyoncé ticket rush begins as pre-sale opens for UK tour dates	https://www.bbc.co.uk/news/entertainment-arts-64496382?at_medium=RSS&at_campaign=KARANGA	album	2023-02-02 16:46:33
ニュース	BBC News - Home	Eurovision: Liverpool stage inspired by a wide hug, BBC says	https://www.bbc.co.uk/news/uk-england-merseyside-64499765?at_medium=RSS&at_campaign=KARANGA	ukraine	2023-02-02 16:46:11
ニュース	BBC News - Home	The row over one of the most famous shirts in football history	https://www.bbc.co.uk/news/uk-scotland-glasgow-west-64496296?at_medium=RSS&at_campaign=KARANGA	england	2023-02-02 16:33:45
ニュース	BBC News - Home	Delilah: Rugby fans torn over WRU choir ban on Tom Jones song	https://www.bbc.co.uk/news/uk-wales-64497520?at_medium=RSS&at_campaign=KARANGA	delilah	2023-02-02 16:27:53
GCP	Cloud Blog	Advancing cancer research with public imaging datasets from the National Cancer Institute Imaging Data Commons	https://cloud.google.com/blog/topics/developers-practitioners/advancing-cancer-research-public-imaging-datasets-national-cancer-institute-imaging-data-commons/	Advancing cancer research with public imaging datasets from the National Cancer Institute Imaging Data CommonsMedical imaging offers remarkable opportunities in research for advancing our understanding of cancer discovering new non invasive methods for its detection and improving overall patient care Advancements in artificial intelligence AI in particular have been key in unlocking our ability to use this imaging data as part of cancer research Development of AI powered research approaches however requires access to large quantities of high quality imaging data  Sample images from NCI Imaging Data Commons  Left Magnetic Resonance Imaging MRI of the prostate credit along with the annotations of the prostate gland and substructures  Right highly multiplexed fluorescence tissue imaging of melanoma credit The US National Cancer Institute NCI has long prioritized collection curation and dissemination of comprehensive publicly available cancer imaging datasets Initiatives like The Cancer Genome Atlas TCGA and Human Tumor Atlas Network HTAN to name a few work to make robust standardized datasets easily accessible to anyone interested in contributing their expertise students learning the basics of AI engineers developing commercial AI products researchers developing innovative proposals for image analysis and of course the funders evaluating those proposals Even so there continue to be challenges that complicate sharing and analysis of imaging data Data is spread across a variety of repositories which means replicating data to bring it together or within reach of tooling such as cloud based resources Images are often stored in vendor specific or specialized research formats which complicates analysis workflows and increases maintenance costs Lack of a common data model or tooling make capabilities such as search visualization and analysis of data difficult and repository or dataset specific  Achieving reproducibility of the analysis workflows a critical function in research is challenging and often lacking in practice Introducing Imaging Data CommonsTo address these issues as part of the Cancer Research Data Commons CRDC initiative that establishes the national cancer research ecosystem NCI launched the Imaging Data Commons IDC a cloud based repository of publicly available cancer imaging data with several key advantages Colocation Image files are curated into Google Cloud Storage buckets side by side with on demand computational resources and cloud based tools making it easier and faster for you to access and analyze Format Images annotations and analysis results are harmonized into the standard DICOM Data Imaging and Communications and Medicine format to improve interoperability with tools and support uniform processing pipelines Tooling IDC maintains tools that without having to download anything allow you to explore and search the data and visualize images and annotations You can easily access IDC data from the cloud based tools available in Google Cloud such as Vertex AI Colab or deploy your own tools in highly configurable virtual environments Reproducibility Sharing reproducible analysis workflows is streamlined through maintaining persistent versioned data that you can use to precisely define cohorts used to train or validate algorithms which in turn can be deployed in virtual environments that can provide consistent software and hardware configuration IDC ingests and harmonizes de identified data from a growing list of repositories and initiatives spanning a broad range of image types and scales cancer types and manufacturers A significant portion of these images are accompanied by annotations and clinical data  For a quick summary of what is available in IDC check the IDC Portal or this Looker Studio dashboard  Exploring the IDC dataIDC PortalA great place to start exploring the data is the IDC Portal From this in browser portal you can use some of the key metadata attributes to navigate the images and visualize them Navigating the IDC portal to view dataset imagesAs an example here are the steps you can follow to find slide microscopy images for patients with lung cancer From the IDC Portal proceed to “Explore images In the top right portion of the exploration screen use the summary pie chart to select Chest primary site you could alternatively select Lung noting that annotation of cancer location can use different terms In the same pie chart summary section navigate to Modality and select Slide Microscopy In the right hand panel scroll to the Collections section which will now list all collections containing relevant images Select one or more collections using the checkboxes  Navigate to the Selected Cases section just below where you will find a list of patients within the selected collections that meet the search criteria  Next select a given patient using the checkbox Navigating to the Selected Studies section just below will now show the list of studies think of these as specific imaging exams available for this patient  Click the “eye icon on the far right which will open the viewer allowing you to see the images themselves BigQuery Public DatasetWhen it s time to search and select the subsets or cohorts of the data that you need to support your analysis more precisely you ll head to the public dataset in BigQuery This dataset contains the comprehensive set of metadata available for the IDC images beyond the subset contained in the IDC portal which you can use to precisely define your target data subset with a custom standard SQL query You can run these queries from the in browser BigQuery Console by creating a BigQuery sandbox The BigQuery sandbox enables you to query data within the limits of the Google Cloud free tier without needing a credit card If you decide to enable billing and go above the free tier threshold you are subject to regular BigQuery pricing However we expect most researchers needs will fit within this tier  To get started with an exploratory query you can select studies corresponding to the same criteria you just used in your exploration of the IDC Portal code block StructValue u code u SELECT r n DISTINCT StudyInstanceUID r nFROM r n bigquery public data idc current dicom all r nWHERE r n tcia tumorLocation Chest r n AND Modality SM u language u lang sql u caption lt wagtail wagtailcore rich text RichText object at xeee gt Alright now you re ready to write a query that creates precisely defined cohorts This time we ll shift from exploring digital pathology images to subsetting Computed Tomography CT scans that meet certain criteria The following query selects all files identified by their unique storage path in the gcs url column and corresponding to CT series that have SliceThickness between and mm It also builds a URL in series viewer url that you can follow to visualize the series in the IDC Portal viewer For the sake of this example the results are limited to only one series code block StructValue u code u SELECT r n collection id r n PatientID r n SeriesDescription r n SliceThickness r n gcs url r n CONCAT StudyInstanceUID seriesInstanceUID SeriesInstanceUID AS series viewer url r nFROM r n bigquery public data idc current dicom all r nWHERE r n SeriesInstanceUID IN r n SELECT r n SeriesInstanceUID r n FROM r n bigquery public data idc current dicom all r n WHERE r n Modality CT r n AND SAFE CAST SliceThickness AS FLOAT gt r n AND SAFE CAST SliceThickness AS FLOAT lt r n LIMIT r n u language u lang sql u caption lt wagtail wagtailcore rich text RichText object at xeff gt As you start to write more complex queries it will be important to familiarize yourself with the DICOM format and how it is connected with the IDC dataset This getting started tutorial is a great place to start learning more What can you do with the results of these queries For example You can build the URL to open the IDC Portal viewer and examine individual studies as we demonstrated in the second query above You can learn more about the patients and studies that meet this search criteria by exploring what annotations or clinical data available accompanying these images The getting started tutorial provides several example queries along these lines You can link DICOM metadata describing imaging collections with related clinical information which is linked when available This notebook can help in navigating clinical data available for IDC collections Finally you can download all images contained in the resulting studies Thanks to the support of Google Cloud Public Dataset Program you are able to download IDC image files from Cloud Storage without cost Integrating with other Cloud toolsThere are several Cloud tools we want to mention that can help in your explorations of the IDC data Colab Colab is a hosted Jupyter notebook solution that allows you to write and share notebooks that combine text and code download images from IDC and execute the code in the cloud with a free virtual machine You can expand beyond the free tier to use custom VMs or GPUs while still controlling costs with fixed monthly pricing plans Notebooks can easily be shared with colleagues such as readers of your academic manuscript Check out these example Colab notebooks to help you get started Vertex AI Vertex AI is a platform to handle all the steps of the ML workflow Again it includes managed Jupyter notebooks but with more control over the environment and hardware you use As part of Google Cloud it also comes with enterprise grade security which may be important to your use case especially if you are joining in your own proprietary data Its Experiments functionality allows you to automatically track architectures hyperparameters and training environments to help you discover the optimal ML model faster  Looker Studio Looker Studio is a platform for developing and sharing custom interactive dashboards You can create dashboards that are focused on a specific subset of metadata accompanying the images and cater to the users that prefer interactive interface over the SQL queries As an example this dashboard provides a summary of IDC data and this dashboard focuses on the preclinical datasets within the IDC Cloud Healthcare API  IDC relies on Cloud Healthcare API to extract and manage DICOM metadata with BigQuery and to maintain DICOM stores that make IDC data available via the standard DICOMweb interface IDC users can utilize these tools to store and provide access to the artifacts resulting from the analysis of IDC images As an example DICOM store can be populated with the results of image segmentation which could be visualized using a user deployed Firebase hosted instance of OHIF Viewer deployment instructions are available here Next StepsThe IDC dataset is a powerful tool for accelerating data driven research and scientific discovery in cancer prevention treatment and diagnosis We encourage researchers engineers and students alike to get started by following the onboarding steps we laid out in this post familiar yourselves with the data by heading to the IDC portal tailor your cohorts using the BigQuery public dataset and then download the images to analyze with your on prem tools or with Google Cloud services or Colab Getting started with the IDC notebook series should help you get familiar with the resource For questions you can reach the IDC team at support canceridc dev or join the IDC community and post your questions Also see the IDC user guide for more details including official documentation Related ArticleBoost medical discoveries with AlphaFold on Vertex AILearn ways to run AlphaFold on Google Cloud using no cost solutions and guides Read ArticleRelated ArticleMost popular public datasets to enrich your BigQuery analysesCheck out free public datasets from Google Cloud available to help you get started easily with big data analytics in BigQuery and Cloud Read Article	2023-02-02 17:00:00