Scikit-learn might be a bit more popular than Rancher. We know about 28 links to it since March 2021 and only 24 links to Rancher. 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 don't know in which extend you plan to use Kubernetes in the future, but if it is aimed to become several huge production clusters, you should looks into Apps like Rancher: https://rancher.com. Source: over 1 year ago
But I think once you have a good understanding of K8S internal (components, how thing work underlying, etc.), you can use some tool to help you provision / maintain k8s cluster easier (look for https://rancher.com/ and alternatives). Source: almost 2 years ago
A few years, I would have said no. Now, I'm cautiously optimistic about it. Personally, I think that you can use something like Rancher (https://rancher.com/) or Portainer (https://www.portainer.io/) for easier management and/or dashboard functionality, to make the learning curve a bit more approachable. For example, you can create a deployment through the UI by following a wizard that also offers you... - Source: Hacker News / almost 2 years ago
Alternatively, it is also possible to use a multi-cloud or hybrid-cloud approach, which combines several cloud providers or even public and private clouds. Special tools such as Rancher and OpenShift can be very useful to run this type of system. - Source: dev.to / almost 2 years ago
Rancher provides a Rancher authentication proxy that allows user authentication from a central location. With this proxy, you can set the credential for authenticating users that want to access your Kubernetes clusters. You can create, view, update, or delete users through Rancher’s UI and API. - Source: dev.to / almost 2 years 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 / 11 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
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Terraform - Tool for building, changing, and versioning infrastructure safely and efficiently.
OpenCV - OpenCV is the world's biggest computer vision library
Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.
NumPy - NumPy is the fundamental package for scientific computing with Python