
OpenCV
Pandas
Scikit-learn
NumPy
Dataiku
Exploratory
htm.java
Figure Eight
Cloudify
OpenShift
Kubernetes
Heroku
Morpheus
Microsoft Azure
Apache Mesos
Redis
Cloudify provides infrastructure automation using โEnvironment as a Serviceโ technology to deploy and continuously manage any cloud, private data center, or Kubernetes service from one central point while leveraging existing toolchains; Terraform, Ansible, and more. Use Cloudify to import existing automation templates and scripts and automatically convert them into certified environments. Manage them using the Cloudify console or export these environments to ServiceNow and enable users to deploy, continuously manage and maintain them as part of approval workflows.
Key Values: - Speed up deployments of your Test/Dev/Production environments. - Manage customers' heterogeneous cloud environments. - Enable Continuous Updates (Day-2) for your Production environments. - A clean API to work on top of all your tools that can easily be used within ServiceNow. - Manage Kubernetes clusters at scale.
OpenCV
CloudifyBased on our record, OpenCV seems to be a lot more popular than Cloudify. While we know about 62 links to OpenCV, we've tracked only 2 mentions of Cloudify. 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.
OpenCV is the world's largest open-source computer vision library, supported by the non-profit organization, Open Source Computer Vision Foundation. It offers a wide range of algorithms that cover a variety of tasks, from basic image processing to advanced object recognition and motion analysis. - Source: dev.to / 7 months ago
Google's Gemini and other multimodal models also fit here, especially for mixed-input apps. James Allsopp, Founder of Ask Zyro, suggests, "For anything involving images or mixed inputs, tools like Claude 3 Opus (great for handling long context) or Google's Gemini can work well, depending on what you need for your user interface." These frameworks excel in scenarios requiring visual understanding, such as augmented... - Source: dev.to / 11 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 isnโt just a tool, itโs a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that donโt just interpret visuals, but... - Source: dev.to / about 1 year ago
Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / about 1 year ago
Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / over 1 year ago
Cloudify looks interesting if you can stand the price, depends how badly you need the features it offers. Source: about 4 years ago
Cloudify is a platform that automates and manages entire lifecycles of an application or network service. Source: over 4 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
OpenShift - OpenShift gives you all the tools you need to develop, host and scale your apps in the public or private cloud. Get started today.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
NumPy - NumPy is the fundamental package for scientific computing with Python
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.