Deployment.io
Harness
Jenkins
Render UIKit
GitHub Actions
Heroku
Vercel
Nife.io
PyTorch
TensorFlow
Keras
Scikit-learn
NumPy
OpenCV
Pandas
CUDA Toolkit
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 PyTorchDeployment.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, PyTorch seems to be a lot more popular than Deployment.io. While we know about 133 links to PyTorch, we've tracked only 1 mention of Deployment.io. 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.
Deployment.io is an AI-powered, self-serve developer platform that simplifies deployment of complex backend services on AWS. - Source: dev.to / 9 months ago
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / about 1 month ago
With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / about 2 months ago
Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / 2 months ago
8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 4 months ago
Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 4 months ago
Harness - Automated Tests For Your Web App
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Render UIKit - React-inspired Swift library for writing UIKit UIs
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.