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You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS seems to be a lot more popular than Machine Box. While we know about 370 links to Amazon AWS, we've tracked only 5 mentions of Machine Box. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Reminds me of Machine Box (http://machinebox.io). Source: over 1 year ago
Thank you :) I did that to teach dog’s breed to an AI. If you don’t know machine box yet : Https://machinebox.io It seems really cool and easy to use. Source: almost 2 years ago
I think you should go 5 Pi X 5 Jetson Nano’s I haven’t seen many people offloading the Nano’s GPU functionality for ML similar to this Serverless style of product. https://machinebox.io/. Source: almost 3 years ago
For face recognition - CompreFace. Disclaimer - I created it, as an alternative you can use MachineBox, but it's not open source and has limits. Also, I think, you will use some software to control the system, e.g. Frigate or Home Assistant, I think this repository can be useful for you. Source: almost 3 years ago
If you have a really simple application, you can just save the encodings into the files. If not - it's better to use a database. SQL is ok. But for the best results, I would suggest using milvus.io, as it was created for saving vectors and finding the distances (I haven't tried it, though). If your final goal is not to learn face recognition basics, you can just use free ready to use solutions like CompreFace... Source: almost 3 years ago
Heroku runs on top of Amazon Web Services (AWS). Key benefits for me are:. - Source: dev.to / 3 days ago
First navigate to AWS at - https://aws.amazon.com create an account and then on the dashboard search for Amazon SES, click get started and then you should be directed to a dashboard like this. - Source: dev.to / 4 days ago
AWS Account Setup: If you don't have one, you can create a free account. - Source: dev.to / 8 days ago
Amazon Web Services is a leading cloud platform offering a vast array of services, from compute and storage to machine learning and IoT. AWS is known for its scalability, handling anything from small projects to enterprise-level applications. - Source: dev.to / 14 days ago
In this tutorial, I will walk you through building a quick static site by doing a static build using ReactJS & create-react-app, then show you how to deploy that static site on AWS using S3 buckets as well as how to cache it & add SSL certificates with CloudFront CDN & Certificate Manager. - Source: dev.to / 14 days ago
Amazon Machine Learning - Machine learning made easy for developers of any skill level
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Model Zoo - Deploy your machine learning model in a single line of code.
Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.
Nexosis - Easy way for developers to build machine learning apps
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!