投稿時間:2023-06-06 20:30:34 RSSフィード2023-06-06 20:00 分まとめ(33件)

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IT	ITmedia 総合記事一覧	[ITmedia News] Apple「Vision Pro」の発表に出なかった言葉　「VR」「メタバース」	https://www.itmedia.co.jp/news/articles/2306/06/news204.html	apple	2023-06-06 19:14:00
TECH	Techable（テッカブル）	不審URLの危険度を判別。電話・SMS対策アプリ「Whoscall」にURLスキャン機能実装	https://techable.jp/archives/210158	gogolook	2023-06-06 10:00:56
python	Pythonタグが付けられた新着投稿 - Qiita	深層学習・CNNにおける画像データセット作成時の画像重複を一瞬で検知する方法	https://qiita.com/HitoriBotchi/items/bd9565e00c09d34da5e4	深層学習	2023-06-06 19:29:45
js	JavaScriptタグが付けられた新着投稿 - Qiita	元の画面位置にスクロールさせる実装について	https://qiita.com/mitsuyossy/items/24a696e5704a4fb86533	遷移	2023-06-06 19:52:08
js	JavaScriptタグが付けられた新着投稿 - Qiita	プログラマーへの道 #13 オブジェクト（プログラミング入門）のメモ	https://qiita.com/emioiso/items/71b2d772aa7743a04ee3	userarray	2023-06-06 19:19:53
js	JavaScriptタグが付けられた新着投稿 - Qiita	【GAS×ChatGPT】PDFや画像からテキストを自動で抽出し、要約する	https://qiita.com/oktaSI/items/50412093f18773820ddc	chatgpt	2023-06-06 19:00:45
GCP	gcpタグが付けられた新着投稿 - Qiita	GCPの監査ログの出力設定をTerraformでおこなう【GCP/Audit Logs/Terraform】	https://qiita.com/yyyyy__/items/67262cdf1b6b963fe431	gcpauditlogsterraform	2023-06-06 19:06:43
Azure	Azureタグが付けられた新着投稿 - Qiita	Azure GPU(NVadsA10v5)でWindows10のParsec環境を作成する	https://qiita.com/red_picmin/items/55c38d32ab1e2e6ee024	azuregpunvadsav	2023-06-06 19:42:40
技術ブログ	Developers.IO	AWS WAFでネストしたOR/ANDを含むルールを楽して作りたい	https://dev.classmethod.jp/articles/aws-waf-original-nested-rule-easy/	cloudformat	2023-06-06 10:40:08
技術ブログ	Developers.IO	Tableau ServerからBigQueryに接続してみた	https://dev.classmethod.jp/articles/tableau-server-bigquery/	bigquery	2023-06-06 10:38:16
海外TECH	MakeUseOf	How to Install the iOS 17, watchOS 10, and macOS Sonoma Developer Betas	https://www.makeuseof.com/how-to-install-apple-developer-beta-updates/	macos	2023-06-06 10:15:18
海外TECH	MakeUseOf	6 Tips to Improve Your Safari Experience on iPhone	https://www.makeuseof.com/tips-to-improve-safari-experience-on-iphone/	experience	2023-06-06 10:01:17
海外TECH	DEV Community	MLOps 101: A Beginner's Guide to Understanding Machine Learning Model Operations	https://dev.to/phylis/mlops-101-a-beginners-guide-to-understanding-machine-learning-model-operations-2dko	MLOps A Beginner x s Guide to Understanding Machine Learning Model Operations Introduction In the dynamic realm of data science and machine learning the introduction of MLOps Machine Learning Operations has addressed critical challenges that plagued the management and deployment of machine learning models in the past Before MLOps data scientists and organizations faced a range of obstacles that hindered model performance and efficiency Lets explore this scenario Picture a data science team developing a sophisticated machine learning model to predict fraudulent transactions for a banking institution The model exhibits impressive accuracy and potential during the development phase Excitement builds as the team envisions the positive impact the model can have on fraud detection and prevention However as the model moves into production complications arise Without standardized processes the team encounters inconsistencies in model performance across different environments Version conflicts varying dependencies and unforeseen issues compromise the model s reliability and effectiveness Troubleshooting becomes a time consuming process lacking clear traceability of changes made during development Scalability becomes another hurdle As the volume of transactions grows exponentially the model struggles to handle the increased load resulting in delays and compromised accuracy The team lacks the infrastructure and mechanisms to efficiently process and analyze the mounting data limiting the model s scalability Additionally the absence of robust version control creates challenges in managing model iterations Collaboration among team members becomes cumbersome hindering reproducibility and hindering effective teamwork Inefficient deployment processes lead to confusion delays and potential conflicts impeding the overall productivity of the data science team These challenges exemplify the pain points prevalent before the introduction of MLOps Recognizing the need for a cohesive and streamlined approach MLOps emerged as a transformative solution revolutionizing the field of machine learning model management By integrating software engineering best practices DevOps principles and data engineering methodologies MLOps bridges the gap between data science and operations It establishes a framework that ensures reliable scalable and reproducible management of machine learning models throughout their lifecycle from development to deployment and beyond In the following sections we will explore the definition and role of MLOps highlight its significance in managing machine learning models delve into the benefits of adopting MLOps practices and address common challenges associated with its implementation Through understanding MLOps data scientists can overcome previous obstacles and embark on a path of efficient and impactful machine learning model management What is MLOps MLOps short for Machine Learning Operations refers to the practices tools and methodologies that facilitate the management deployment monitoring and scaling of machine learning models in production environments It brings together elements from software engineering DevOps Development and Operations and data engineering to create a streamlined and efficient workflow for handling machine learning models throughout their lifecycle Why is MLOps important MLOps plays a pivotal role in the management of machine learning models ensuring their reliability scalability and reproducibility in real world scenarios Reliability MLOps establishes best practices and processes to ensure that machine learning models perform consistently and reliably in production environments It addresses challenges related to version control dependency management and configuration minimizing the risk of unexpected behavior or failures By implementing rigorous testing monitoring and alerting mechanisms MLOps helps maintain optimal model performance and detect any deviations or issues early on Scalability Machine learning models often face scalability challenges when deployed in production especially with increasing data volumes and user demand MLOps addresses scalability concerns by optimizing infrastructure managing resources efficiently and implementing data pipeline orchestration It ensures that models can handle large scale data processing and deliver timely responses enabling organizations to scale their machine learning applications effectively Reproducibility Reproducing and replicating machine learning models across different environments is vital for validation collaboration and auditability MLOps provides mechanisms for managing code data and model versions making it easier to recreate and reproduce models consistently With proper version control and documentation data scientists can confidently share their work collaborate seamlessly and reproduce models for further development or troubleshooting Collaboration and Efficiency MLOps promotes collaboration between data scientists IT and operations teams By establishing standardized workflows tools and communication channels MLOps enables seamless collaboration throughout the model lifecycle Data scientists can work closely with operational teams to ensure smooth transitions from development to deployment aligning business requirements with technical considerations This collaboration enhances efficiency reduces bottlenecks and accelerates the time to market for machine learning applications Compliance and Governance In regulated industries or organizations with strict compliance requirements MLOps plays a critical role It helps establish governance frameworks data privacy controls and audit trails ensuring adherence to regulatory guidelines and maintaining data integrity MLOps enables organizations to track and manage model versions monitor model performance and implement necessary security measures to safeguard sensitive data MLOps ChallengesWhile MLOps Machine Learning Operations offers numerous advantages in managing machine learning models it is not without its challenges Let s explore some common challenges associated with MLOps and how organizations can address them effectively Data Quality Issues One of the fundamental challenges in MLOps is ensuring the quality and reliability of data used for training and deploying models Inaccurate incomplete or biased data can significantly impact model performance and lead to misleading insights or predictions Organizations must invest in data quality assurance processes including data cleaning preprocessing and validation to mitigate these issues and ensure robust and trustworthy models Model Versioning and Deployment Managing different versions of machine learning models poses a challenge in MLOps Organizations need to establish efficient version control mechanisms to track changes manage dependencies and ensure reproducibility Furthermore deploying new model versions while ensuring minimal disruption and maintaining consistent performance can be complex Implementing robust deployment pipelines automated testing and roll back mechanisms can help address these challenges Compliance and Regulatory Requirements Organizations operating in regulated industries must navigate compliance and regulatory challenges when deploying machine learning models Data privacy security and ethical considerations become critical factors Ensuring compliance with regulations such as GDPR HIPAA or industry specific guidelines requires incorporating appropriate data protection measures audit trails and governance frameworks into the MLOps processes Model Monitoring and Maintenance Once models are deployed continuous monitoring and maintenance become vital Detecting and addressing model drift performance degradation and anomalies in real time is crucial to maintain model effectiveness Organizations need to establish robust monitoring systems implement automated alerting mechanisms and define processes for ongoing model maintenance and updates Cross Team Collaboration Effective collaboration between data scientists IT operations and other stakeholders is vital for successful MLOps implementation Overcoming silos aligning priorities and fostering communication and cooperation can be challenging Establishing cross functional teams promoting knowledge sharing and adopting collaborative tools and practices can help organizations tackle these collaboration challenges I hope you enjoyed this introductory guide to understanding MLOps and its role in managing machine learning models Stay tuned for more articles on MLOps where I will delve deeper into advanced concepts best practices and emerging trends in this field I m excited to share more knowledge and insights to help you navigate the ever evolving landscape of machine learning and MLOps Thank you for reading and I look forward to continuing this MLOps journey together	2023-06-06 10:06:07
Apple	AppleInsider - Frontpage News	Apple Vision Pro developer kits will be made available eventually, and to a select crowd	https://appleinsider.com/articles/23/06/06/apple-vision-pro-developer-kits-will-be-available?utm_medium=rss	Apple Vision Pro developer kits will be made available eventually and to a select crowdDevelopers will be able to apply for an Apple Vision Pro developer kit but no details are available yet Apple Vision ProApple s next platform is spatial computing and the Apple Vision Pro is the first step into that future Developers will need to prepare their apps for the new product line but the headset won t be available until early Read more	2023-06-06 10:48:55
海外TECH	Engadget	Amazon Fire Kids tablets are up to 50 percent off right now	https://www.engadget.com/amazon-fire-kids-tablets-are-up-to-50-percent-off-right-now-103503750.html?src=rss	Amazon Fire Kids tablets are up to percent off right nowAmazon is having a big sale on its Fire Kids tablets just in time for summer break The Fire tablet GB boasts one of the best discounts along with normally being the cheapest option with a percent off sale bringing it to from However for only more the GB model is on sale for down from ーa percent discount nbsp Both memory options are designed for kids ages three to seven and include one free year of Amazon Kids which has ad free books videos games and Alexa skills The kid proof encased tablet also comes with parental controls up to hours of battery life dual cameras and a two year warranty Plus you can always add TB of extra storage with a microSD Amazon is also running sales on its other tablets for three to seven year olds with the Fire HD down from to and the Fire HD down from to They offer many of the same features as the Fire tablet including parental controls a sturdy case and a free year of Amazon Kids The main differences are increased battery life and the screens being larger and in HD nbsp For kids ages six to the Amazon Fire HD Kids Pro tablet is percent off ーon sale for down from The Fire HD Kids Pro lets kids browse the web with tailored parental controls in place They can also call approved Alexa enabled devices and send you requests to buy things like apps and eBooks in the digital store The screen is a inch HD with a kid friendly case Each Fire Kids tablet sale applies to certain available colors nbsp Follow EngadgetDeals on Twitter and subscribe to the Engadget Deals newsletter for the latest tech deals and buying advice This article originally appeared on Engadget at	2023-06-06 10:35:03
海外TECH	Engadget	Meta’s Oversight Board says the company’s rules are (slowly) changing for the better	https://www.engadget.com/metas-oversight-board-says-the-companys-rules-are-slowly-changing-for-the-better-100059192.html?src=rss	Meta s Oversight Board says the company s rules are slowly changing for the betterMore than two years after it formed the Oversight Board says that its recommendations have helped make Meta s rules more transparent to its users though the company still needs to improve in some key areas The board which is made up of nearly two dozen experts in human rights and free speech published its annual report covering its work and interactions with Meta over the last year While last year s report sharply criticized Meta for not being transparent enough the latest report highlights the impact the board s recommendations are starting to have on the company “In it was encouraging to see that for the first time Meta made systemic changes to its rules and how they are enforced including on user notifications and its rules on dangerous organizations the board wrote in a statement The report also highlights areas where its members believe Meta can improve According to the report Meta reversed its initial moderation decision in almost two thirds of cases they were picked for the Oversight Board s shortlist which “raises wider questions both about the accuracy of Meta s content moderation and the appeals process The board also points out that it s been more than two years since it first recommended the company better align its policies between Instagram and Facebook but that the company has “repeatedly pushed back the deadline for doing so The group also takes issue with Meta s refusal to translate internal guidelines for its content moderators into their native languages Meta has contended that all its moderators are fluent in English so the step is unnecessary But the board says “English only guidance may cause reviewers to miss context and nuance across languages and dialects which can cause errors in enforcement The report also describes a lack of transparency over some aspects of Meta s “newsworthiness exception which allows for some rule breaking posts to remain online if the company determines there is a “public interest value in the content The Oversight Board says there is still “little is known about the process it Meta uses to decide whether content is newsworthy and that the company s responses to questions about the policy side stepped direct answers The board s report suggests there are other frustrating moments in its interactions with Meta At one point the board notes that it took eight months for its members to gain access to the company s analytics tool CrowdTangle The board also notes that many of its decisions over the last year were published after the day timeframe set out in its rules The board cites a few reasons for these delays but says that in some cases delays were due to “negotiations with Meta about how much information which was originally provided by the company on a confidential basis we could include in our final decision that took “longer than anticipated It s also notable just how few cases the Oversight Board ends up weighing in on In the report the board says that in it published decisions ーa tiny fraction of the nearly million requests it received from users hoping to overturn one of Meta s moderation decisions The board notes that it purposely chooses cases its members believe will have a large impact on Meta s user base Even so numbers underscore the fact that that board will never be able to address the vast majority of requests it receives despite more than million in funding from Meta That said the Oversight Board has said that it intends to move faster on some cases and that it will issue fast tracked “summary decisions in some cases beginning this year Interestingly the report also touches on Meta s suggestion that other social media companies should consider using the Oversight Board “We are interested in working with companies that share our belief that transparent and accountable content governance overseen by independent bodies is an essential part of creating an online environment that respects freedom of expression and other human rights the report says It s still unclear how Meta s peers would start working with the group ーor if they have any desire to do so ーbut the board clearly thinks it has learnings other companies could benefit from “We re not seeking to be the board for the whole industry Oversight Board Director Thomas Hughes says in the report “But we are seeking to share what we ve learned and work with companies interested in setting up different bodies to set standards and oversee content governance This article originally appeared on Engadget at	2023-06-06 10:00:59
海外TECH	WIRED	AI Could Usher in a New Era of Music. Will It Suck?	https://www.wired.com/story/fake-drake-will-ai-music-suck/	artists	2023-06-06 11:00:00
海外TECH	WIRED	Apple Ghosts the Generative AI Revolution	https://www.wired.com/story/apple-ghosts-the-generative-ai-revolution/	Apple Ghosts the Generative AI RevolutionApple unveiled the Vision Pro headset and a number of AI powered features yesterday but largely ignored generative AI applications embraced by Google and Microsoft	2023-06-06 11:00:00
海外TECH	WIRED	The Quest for a Switch to Turn on Hunger	https://www.wired.com/story/the-quest-for-a-switch-to-turn-on-hunger/	drugs	2023-06-06 11:00:00
海外TECH	WIRED	Do People Actually Want to Wear a Headset All the Time?	https://www.wired.com/story/do-people-actually-want-to-wear-vr-headsets/	digital	2023-06-06 11:00:00
ニュース	BBC News - Home	Tottenham: Ange Postecoglou leaves Celtic to become new Spurs manager	https://www.bbc.co.uk/sport/football/65819185?at_medium=RSS&at_campaign=KARANGA	Tottenham Ange Postecoglou leaves Celtic to become new Spurs managerTottenham confirm the appointment of Ange Postecoglou as their new manager with the Australian leaving Celtic to take over at the Premier League club	2023-06-06 10:27:26
ニュース	BBC News - Home	CBI: Business group giant faces crunch vote to survive	https://www.bbc.co.uk/news/business-65809060?at_medium=RSS&at_campaign=KARANGA	lobby	2023-06-06 10:07:58
ニュース	BBC News - Home	Robert Hanssen: The fake job that snared FBI agent who spied for Moscow	https://www.bbc.co.uk/news/world-us-canada-65820220?at_medium=RSS&at_campaign=KARANGA	spies	2023-06-06 10:19:23
ニュース	BBC News - Home	Lord John Morris, ex-Welsh secretary and Blair attorney general dies	https://www.bbc.co.uk/news/uk-wales-politics-65484126?at_medium=RSS&at_campaign=KARANGA	fruition	2023-06-06 10:47:00
ニュース	BBC News - Home	How Postecoglou made his 'fantasy football world' a reality	https://www.bbc.co.uk/sport/football/65509996?at_medium=RSS&at_campaign=KARANGA	How Postecoglou made his x fantasy football world x a realityFrom escaping a military junta in Greece as a five year old to rejuvenating Celtic Ange Postecoglou s journey has been a remarkable one writes Tom English	2023-06-06 10:07:55
ニュース	BBC News - Home	Ukraine dam: Thousands evacuated from ‘critical zone’ near Kakhovka plant	https://www.bbc.co.uk/news/world-europe-65819591?at_medium=RSS&at_campaign=KARANGA	ukraine	2023-06-06 10:18:25
ニュース	BBC News - Home	Why is the Kakhovka dam important?	https://www.bbc.co.uk/news/world-europe-65818705?at_medium=RSS&at_campaign=KARANGA	kherson	2023-06-06 10:19:21
ニュース	BBC News - Home	Ukraine dam: Swans seen swimming though Nova Kakhovka	https://www.bbc.co.uk/news/world-europe-65818722?at_medium=RSS&at_campaign=KARANGA	kakhovkathe	2023-06-06 10:31:48
IT	週刊アスキー	赤いスカーフと旅しよう！『SCARF』がPS5／PS4／XSX|S／Xbox Oneで7月6日に配信決定	https://weekly.ascii.jp/elem/000/004/139/4139863/	handygames	2023-06-06 19:55:00
IT	週刊アスキー	Steam版『ジャンボ空港物語』と『発見どうぶつ公園』が配信中！	https://weekly.ascii.jp/elem/000/004/139/4139858/	pcsteam	2023-06-06 19:30:00
IT	週刊アスキー	アドバンスト・メディア「AmiVoice ScribeAssist」を活用　リアルタイムに字幕表示できる透明ディスプレーを取手市障害福祉課の窓口に導入	https://weekly.ascii.jp/elem/000/004/139/4139823/	アドバンスト・メディア「AmiVoiceScribeAssist」を活用リアルタイムに字幕表示できる透明ディスプレーを取手市障害福祉課の窓口に導入アドバンスト・メディアのAI音声認識文字起こし支援アプリケーション「AmiVoiceScribeAssist」の字幕ポップアップ機能を活用した会話をリアルタイムに字幕表示できる透明ディスプレー「Rælclearレルクリア」を取手市の障害福祉課の窓口に導入。	2023-06-06 19:45:00
IT	週刊アスキー	赤ちゃんの笑顔を引き出すHonda NSXのエンジン音　タカラトミーアーツ「赤ちゃんスマイル Honda SOUND SITTER」10月28日発売	https://weekly.ascii.jp/elem/000/004/139/4139816/	honda	2023-06-06 19:30:00
GCP	Cloud Blog	Predict, personalize, and wow your customers with better analytics and AI	https://cloud.google.com/blog/topics/financial-services/predict-and-personalize-your-financial-services-with-analytics-and-ai/	Predict personalize and wow your customers with better analytics and AIWhat will financial services look like in the future Historically financial services have been driven by one on one relationship based interactions  Now customers expect these trusted relationships to translate into personalized digital experiences that prove their providers know and value them and have the capabilities to help them It s no secret that customer engagement leads to revenue growth That s why customer data platforms CDPs have gained traction across multiple industries to help businesses analyze predict and personalize customer journeys However a CDP by itself may not be sufficient to gain a holistic view of the customer Financial services institutions already capture a myriad of customer and marketing data and use many tools to engage their customers But companies still struggle to unify and analyze all that data across silos and create a holistic view of their customers that s actionable across different channels and capabilities Financial services companies will need to think strategically about maximizing their use of first party data and analytics to drive better and sustainable business outcomes With a true view of the customer financial services institutions can seamlessly combine all the right data and gain meaningful insights to deliver the type of personalization that really matters to customers Google Cloud is here to helpCustomer expectations are changing rapidly and financial services leaders need to be as innovative and efficient as possible By combining customer servicing and transactional data with marketing channel CRM and other first party data within a secure cloud based environment equipped with intelligent ML driven customer insights financial services companies can uncover deeper customer insights and activate them quickly To learn more or explore how Google Cloud fits into your customer data technology strategy please contact us or visit our website aside block StructValue u title u White paper Lead the way in customer focused financial services with analytics and AI u body lt wagtail wagtailcore rich text RichText object at xebc gt u btn text u Download now u href u u image None Footnotes “The value of getting personalization rightーor wrongーis multiplying McKinsey amp Company November  “Survey shows customers give banks lackluster grades on their personalization Insider Intelligence April “The Future of Insurance Personalized Digitized and Connected Capco Insurance September Press Release “ of Consumers Would Share Personal Data to Get Cheaper Insurance Premiums Capco July   “The value of getting personalization rightーor wrongーis multiplying McKinsey amp Company November “ Today Segment com “Getting Personal Consumer Perspectives on AI in Marketing and Customer Service CDP com April   Gartner Gartner s Customer Data Survey The Degree View of the Customer Is More Myth Than Reality Benjamin Bloom Lizzy Foo Kune Mike McGuire November GARTNER is a registered trademark and service mark of Gartner Inc and or its affiliates in the U S and internationally and MAGIC QUADRANT is a registered trademark of Gartner Inc and or its affiliates and are used herein with permission All rights reserved “Closing the data value gap Accenture December “Reshaping retail banking for the next normal McKinsey amp Company “Bring Customers Into Clear Focus Gallup January “Responsible Marketing With First Party Data BCG May aside block StructValue u title u How financial services can unlock customer insights to deliver personalization securely u body lt wagtail wagtailcore rich text RichText object at xebfbcc gt u btn text u Read now u href u u image lt GAEImage fsi x transform jpg gt	2023-06-06 10:30:00