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Start scripts for kubernetes native nodes These statup scripts require a UBUNTU 20.04 LTS base image! If you want to use Ubuntun 22.04 LTS, exchange for the kubenretes part ‘xenial’ with ‘jummy’. Script for the master node on AWS #!/bin/bash # Load necessary kernel modules for containerd cat << EOF | sudo tee /etc/modules-load.d/containerd.conf overlay br_netfilter EOF sudo modprobe overlay sudo modprobe br_netfilter # Configure kernel networking requirements for Kubernetes cat << EOF | sudo tee /etc/sysctl....

AWS multiple accounts/profiles If you’re working with multiple AWS accounts or environments, using named profiles with the AWS CLI can be very handy. Named profiles are configurations of AWS access keys, secret keys, and default regions. Here’s how you can set up and use multiple profiles: Setting Up Multiple Profiles You can add profiles directly to your AWS configuration file (~/.aws/config) and your credentials file (~/.aws/credentials). Here’s an example for ~/....

Autoregressive integrated moving average (ARIMA) Autoregressive integrated moving average (ARIMA) is a statistical model that is used to forecast future values of a time series. It is a generalization of the autoregressive moving average (ARMA) model, which only considers the autocorrelations between the current value and the past values of the time series. ARIMA also considers the moving average of the residuals of the ARMA model, which helps to improve the accuracy of the forecasts....

Softmax Activation Function The softmax activation function is a non-linear function that is commonly used in the output layer of neural networks for multi-class classification problems. It takes a vector of real numbers as input and outputs a vector of probabilities, where the probabilities sum to 1. This means that the softmax function can be used to represent a probability distribution over the possible output classes. The softmax function is defined as follows:...

Analytics Random Cut Forest (RCF) Analytics Random Cut Forest (RCF) is an unsupervised machine learning algorithm that is used for anomaly detection. In RCF, each tree is constructed by randomly selecting a subset of features and then randomly selecting a subset of data points from the training dataset. This process helps to ensure that the trees are more diverse and less correlated, which makes them more effective at detecting anomalies....