投稿時間:2022-07-24 17:07:43 RSSフィード2022-07-24 17:00 分まとめ(8件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
AWS	lambdaタグが付けられた新着投稿 - Qiita	Lambda関数をローカル環境からワンタッチでデプロイする	https://qiita.com/jun_knd_1300/items/181b966044067a75ee9d	lambda	2022-07-24 16:05:02
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS「ポリシーの評価論理フローチャート」（日本語版）	https://qiita.com/tsukamoto/items/e069ca89d1b78a6e5c1f	日本語版	2022-07-24 16:38:08
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】present?を少し理解してみる（クイズあり）	https://qiita.com/koki_73/items/0852d89e79dddeb70a8b	blank	2022-07-24 16:06:32
技術ブログ	Developers.IO	EventBridgeを使用して自動EBSスナップショットを作成してみた。	https://dev.classmethod.jp/articles/automated-amazon-ebs-snapshots-using-eventbridge/	eventbridge	2022-07-24 07:17:50
海外TECH	DEV Community	SageMaker on AWS: Tips for Machine Learning Success	https://dev.to/chainguns/sagemaker-on-aws-tips-for-machine-learning-success-1e40	SageMaker on AWS Tips for Machine Learning Success SageMaker on AWS Tips for Machine Learning SuccessIt is important to have a clear goal in mind for your machine learning project What do you want to achieve with your machine learning model Once you have a goal you can start to experiment with different algorithms and parameters to find the best results SageMaker includes many different algorithms that can be used for different types of machine learning tasks For example there are algorithms for classification regression and clustering Finally it is important to experiment with different algorithms and parameters to find the best results SageMaker includes many different algorithms that can be used for different types of machine learning tasks For example there are algorithms for classification regression and clustering Each algorithm has different parameters that can be adjusted to improve performance It is important to try out different combinations of algorithms and parameters to find the best results for your machine learning task SageMaker OverviewSageMaker is powered by Amazon Web Services AWS and provides developers with the ability to build train and deploy machine learning models quickly and easily SageMaker removes the complexity of building and maintaining a machine learning infrastructure making it easy for developers to get started with machine learning SageMaker is a managed service that means that AWS takes care of all the undifferentiated heavy lifting required to set up and run a machine learning model at scale This includes provisioning compute resources storing and accessing data managing dependencies monitoring training jobs deploying models and more All you need to do is provide your data and specify your desired model configuration SageMaker makes it easy to get started with machine learning by providing prebuilt algorithms that can be used outofthebox or customized according to your needs You can also bring your own algorithms to SageMaker and take advantage of the managed compute resources that SageMaker offers Whether you re just getting started with machine learning or are looking for a way to simplify your workflows SageMaker can help you achieve your goals Getting Started with SageMakerOnce you have the right tools and resources in place the next step is to take advantage of SageMaker s capabilities to automate and optimize your machine learning models SageMaker provides a number of features that can help with this including automatic model tuning and automatic instance selection By taking advantage of these features you can save yourself a lot of time and effort when it comes to developing and deploying machine learning models Finally keep an eye on your costs when using SageMaker Machine learning can be expensive so it s important to keep an eye on your spending Fortunately SageMaker provides a number of costsaving features such as spot instances and reserved instances By taking advantage of these features you can minimize your costs without sacrificing performance My Pro Gamer Moves in SagemakerI ll skip explaining all the setup of IAM Roles and the notebook because there are hundreds of tutorials that do that what I want to focous on are my pain points with SageMaker and the good things of course My first tip is the docs are your best friend whether you are using the SDK or Sagemaker studio you will have to constantly refactor your ML Model to Sagemaker s format Follow the docs closely Another important and similar tip is to make good use of the massive aws samples github repo there are many exmaples of impleneted models there as well as deafult functons for some of the things sagemaker doesn t show and many more great resources just make sure to search for sagemaker realted repos One more helpful tip from me is to create lifecycle configuration scripts for you as soon as possible you don t want to have to install all your packages everytime the notebook starts and you can also add things like auto notebook shutdown so you don t waste your comapny hunderds of dollars over ther weekend again some of the are availiable on the aws samples repo SageMaker on AWS is a powerful tool for machine learning success But like any tool it s only as good as the person using it A few closing notes Choose your data wisely SageMaker on AWS can handle large amounts of data but that doesn t mean you should just throw every dataset you can find at it Be thoughtful about which data will be most helpful in training your machine learning models Don t be afraid to experiment Trying out different algorithms hyperparameter settings and data processing techniques is essential to finding the right solution for your problem Don t be afraid to experiment and fail it s all part of the learning process Stay organized With so many moving parts it s easy to lose track of what you ve tried and what s worked and what hasn t Keeping a clear and organized log of your experiments will save you time and headaches down the road Following these tips will help set you up for success as you begin your journey with SageMaker on AWS Check out our website at BLSTJoin the discussion in our Discord channelTest your API for free now at BLST	2022-07-24 07:46:04
海外科学	NYT > Science	China Launches Wentian Space Station Module With Giant Rocket	https://www.nytimes.com/2022/07/24/science/china-space-rocket-long-march.html	booster	2022-07-24 07:22:34
ニュース	BBC News - Home	Dover and Eurotunnel queues: Travellers warned of third day of delays	https://www.bbc.co.uk/news/uk-62281443?at_medium=RSS&at_campaign=KARANGA	eurotunnel	2022-07-24 07:52:51
北海道	北海道新聞	道南在住３５１人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/709555/	道南	2022-07-24 16:31:34