Rupesh Tiwari rupeshtiwari

Perfect, here's your setup with your exact details filled in:

1. Generate the key pair

ssh-keygen -t ed25519 -C "roopkt@gmail.com"

flowchart LR

    subgraph Clients
        D1[Wearable Device]
        D2[Mobile App]
    end

    subgraph Ingestion
        AGW[API Gateway / Load Balancer]

Agnoster prompt fixes
New-line arrow cursor
tmux green-bar fix

Clean Agnoster Prompt + Hidden tmux Status Bar (Mac / zsh)

brew install yt-dlp


yt-dlp --verbose --allow-unplayable-formats --merge-output-format mp4 -f "bv*+ba/b" "https://classes.gdvpanel.in/guruji-live-classes/01/dash/stream.mpd"

Let's walk through the full steps to generate a new personal_rsa key for your personal projects (if it doesn't already exist), set up the SSH configuration for both accounts, and smoothly switch between them.

Step 1: Check for Existing SSH Keys

First, let's check if you already have a personal SSH key, such as personal_rsa or id_rsa.

Run this command to list the SSH keys in your ~/.ssh/ directory:

ls ~/.ssh/

To build a short demo for AWS Forecast, you’ll want to focus on the key steps involved in setting up, training, and generating predictions. Here's a simple guide to create a short demo:

1. Define the Scope of the Demo (5-10 minutes)

Objective: Show how to upload historical data, train a model, and generate predictions using AWS Forecast.
Example Use Case: Predict demand for a product based on historical sales data.

2. Prepare Your Data (1 minute)

Use a simple time-series dataset (e.g., historical sales, demand data, or temperature).
Format the dataset as CSV with columns such as timestamp, item_id, and demand.
Make sure the dataset is in a format acceptable by AWS Forecast (e.g., Amazon S3 CSV file).

1. Installing kubectl on macos

https://kubernetes.io/docs/tasks/tools/install-kubectl-macos/

2. Installing Docker Engine

https://docs.docker.com/desktop/install/mac-install/#install-from-the-command-line

Preparing for the AWS Certified Machine Learning – Specialty Exam: Key Concepts and Tools

Earning the AWS Certified Machine Learning – Specialty certification requires a solid understanding of various machine learning concepts, tools, and AWS services. Here are some of the key topics and resources that helped me prepare for the exam.

What is classifier?

In Amazon SageMaker, a classifier is a type of machine learning model that categorizes or classifies data into distinct classes or categories based on input features. Here are the key concepts related to classifiers and the metrics used to evaluate them:

Key Metrics for Classifiers

Accuracy:

	# Enable Powerlevel10k instant prompt. Should stay close to the top of ~/.zshrc.
	# Initialization code that may require console input (password prompts, [y/n]
	# confirmations, etc.) must go above this block; everything else may go below.
	#if [[ -r "${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh" ]]; then
	# source "${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh"
	#fi

	# If you come from bash you might have to change your $PATH.
	# export PATH=$HOME/bin:/usr/local/bin:$PATH

	#!/bin/bash

	# How to use:
	# chmod +x backup_android_all_incremental.sh
	# ./backup_android_all_incremental.sh
	# ./backup_android_all_incremental.sh --silent OR -s

	# --------------
	# Parse args
	SILENT=0