Deployment.io
Harness
Jenkins
GitHub Actions
Render UIKit
Heroku
Vercel
Netlify
Roboflow Universe
TensorFlow Lite
Apple Core ML
Aquarium
Spacelift.io
Monitor ML
Google CLOUD AUTOML
Qualdo™
Deployment simplifies continuous code integration and delivery automation for startups and agile engineering teams on the AWS cloud, eliminating the need for DevOps engineering. A developer can deploy static sites, web services, and environments without knowledge of AWS or DevOps. Deployment supports previews on pull requests and automatic deployments on code push without manual setup or scripting. It enables engineering teams to focus on tasks that add customer value instead of worrying about DevOps-related grunt work.
Deployment.io Roboflow UniverseNo features have been listed yet.
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Deployment.io's answer
I led engineering teams at early-stage startups and realized that startups waste 70% of valuable engineering time on tedious, non-coding tasks that they can easily automate.
To solve this problem, we've built Deployment.io so engineering teams at startups can focus on writing more code that adds value and helps them achieve PMF faster.
Deployment.io's answer
ReactJs using Typescript, GatsbyJs using Typescript, GoLang, and AWS
Deployment.io's answer
Deployment.io is built and designed for startups. Our customers can onboard in 5 minutes and start deploying apps to AWS without any DevOps or AWS knowledge. Other platforms are complex and require scripting or DevOps knowledge. They are built for bigger companies with a lot of resources.
Deployment.io's answer
Startups and agile engineering teams should choose Deployment.io for the simplicity and ease of use. Our competitors are complex and are designed for bigger companies.
Deployment.io's answer
For startups, speed and focus are crucial. Our primary audience is engineering teams at startups that want to focus on building code that adds value and not on DevOps related grunt work.
Deploying web apps on AWS has never been this easy and it also takes care of scaling based on usage.
Based on our record, Roboflow Universe seems to be more popular. It has been mentiond 18 times since March 2021. 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.
It’s an easy to use inference server for computer vision models. The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface). It’s backed by a bunch of component pieces: * a server (so you don’t have to reimplement things like image processing & prediction... - Source: Hacker News / 9 months ago
* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects. Source: about 1 year ago
For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com. - Source: Hacker News / over 1 year ago
Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers?ref=whoishiring1222 Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment. Over 100k engineers (including engineers from 2/3... - Source: Hacker News / over 1 year ago
Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com. Source: over 1 year ago
Harness - Automated Tests For Your Web App
TensorFlow Lite - Low-latency inference of on-device ML models
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Apple Core ML - Integrate a broad variety of ML model types into your app
GitHub Actions - Automate your workflow from idea to production
Aquarium - Improve ML models by improving datasets they’re trained on