Kubeflow VS Scikit-learn

Kubeflow Reviews

Scikit-learn Reviews

Kubeflow mentions (2)

• The Bacalhau Vision – A Distributed Compute over Data Platform

  I'm David Aronchick - first non-founding PM on Kubernetes, co-founder of Kubeflow [1], and co-founder of the SAME project [2] - and we've spent the past year working on Bacalhau [3], an open source project to bring compute to data. We've recently opened up a public-hosted cluster (all runnable from colab in our docs [4]) and would love your feedback - you can see our vision at the attached blog post. Thanks!... - Source: Hacker News / about 1 year ago

• An update on relationships between stocks - STATISTICS ROCKS! - Brought to you by the SuperstonkQuants 🦍🥼🔬🚀

  You have GitHub org and a Vue based website up and running already, so it seems like you have tech logistics covered. Just in case it's useful, I have experience with Kubernetes, which can help run computationally intense workloads (even if GPUs are needed) or provide a pool of compute for something like Kubeflow (kubeflow.org). Here if you want, feel free to ignore if you're all covered in this area - I'll be... Source: almost 3 years ago

Scikit-learn mentions (27)

• Link Prediction With node2vec in Physics Collaboration Network

  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

• WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point

  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: 12 months ago

• PSA: You don't need fancy stuff to do good work.

  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: 12 months ago

• Help on using R for Machine Learning?

  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

• Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service

  This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago