Unimus
Oxidized
RANCID
GenieACS
NetDisco
rConfig
Kiwi CatTools
Backbox.co
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
Exploratory
htm.java
Unimus is a multi-vendor NCM software that covers these four main areas:
Network Automation - Deploy configuration network-wide with just a few clicks with the Mass Config Push / Pull features available in Unimus.
Disaster Recovery - Automatic, continuous configuration backup with notifications on failure. Your network will be prepared for any unforeseen circumstances.
Change Management - Easy change management with graphical diffs in only a few clicks. Unimus makes change-tracking and change-auditing an easy task.
Configuration Auditing - Gain visibility into your network. Search your entire networks configuration in seconds to know what is configured how and where.
Unimus Scikit-learnNo features have been listed yet.
Unimus's answer
Unimus is an on-premise, multi-tenant, device agnostic NCM software that brings value and saves time. Disaster recovery and Change management together with Configuration auditing and Network Automation features, make Unimus a very robust network configuration management system.
Unimus's answer
Unimus came to this world in 2016. Our goal was to create a simple, user friendly, but powerful Network Automation and Network Config Management solution. Unimus now manages more than a million network devices across thousands of deployments around the world.
Our mission has since expanded to bring other new tools which are missing in the Networking industry to the market. We want to create software that will make life easier for net-admins around the world.
Scikit-learn might be a bit more popular than Unimus. We know about 28 links to it since March 2021 and only 19 links to Unimus. 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.
I recently found out about unimus. It really works well to push configs and gather configs - you can see the changes for each config pull even across different devices. It runs as .exe or on a vm Check it out! Not even expensive - 1device 4,5€ a year or 7500€ a year unlimited. Source: about 1 year ago
Unimus would handle this nicely for you. It will build a versioned configuration history for your devices, and you can then see changepoints - when something changed, and what changed (including nice graphical diffs). Source: about 1 year ago
Take a look at Unimus. It will generate a configuration timeline for your devices, you can generate diffs, and it will send config change notifications (including full graphical diffs in the change notification emails / Slack notifications). Also many other useful config management features in there. Source: about 1 year ago
I forgot also Unimus. They are amazing 🤩. https://unimus.net. Source: about 1 year ago
If you have zero netops experience (eg ansible) this will work: https://unimus.net/. Source: about 1 year ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 12 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
Oxidized - configuration backup software (IOS, JunOS) - silly attempt at rancid
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
RANCID - RANCID - Really Awesome New Cisco confIg Differ.
OpenCV - OpenCV is the world's biggest computer vision library
GenieACS - A fast and lightweight TR-069 Auto Configuration Server (ACS)
NumPy - NumPy is the fundamental package for scientific computing with Python