Bunnyshell
Heroku
Porter
Okteto
8base
Flynn
YunoHost
Render UIKit
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
Bunnyshell automates all steps in the release process, from creating servers on multiple clouds (AWS, Azure, Google Cloud, Digital Ocean) to easy provisioning (ready to use apps - install & configure with one click) and one click deployments.
We are helping companies save time and money by standardizing and automating otherwise time consuming, knowledge-dependant or prone to error infrastructure-related tasks.
With Bunnyshell and a few clicks, any developer can:
Migrate easily (from premise to cloud, cloud to cloud) Create servers on multiple clouds Provision & configure applications Deploy with one click and zero downtime (multiple deployments time) Version their work and rollback any time Create dev & test environments on any cloud, version, OS Have automated security updates for all projects
BunnyshellBased on our record, NumPy seems to be a lot more popular than Bunnyshell. While we know about 122 links to NumPy, we've tracked only 2 mentions of Bunnyshell. 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.
Https://bunnyshell.com and k8s -- seems like a good way to get going quickly with new projects --. - Source: Hacker News / almost 3 years ago
With Infrastructure as Code at its current state of maturity, itโs now easier than ever to replicate microservice environments in the cloud. This unlocked a new approach of having a personal production-like cloud environment for every developer, which they can use freely and in isolation. It comes in two flavors - persistent environments, or ephemeral environments created on demand with products like Okteto or... - Source: dev.to / over 3 years ago
Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
AI starts with math and coding. You donโt need a PhDโjust high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Pythonโs syntax is straightforward. - Source: dev.to / 12 months ago
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / over 1 year ago
This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / almost 2 years ago
Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Porter - Heroku that runs in your own cloud
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Okteto - Development platform for Kubernetes applications.
OpenCV - OpenCV is the world's biggest computer vision library