Python Programming For Mlops - Aiops - Devops

Optimize MLOps, AIOps, and DevOps Workflows with Python

What you'll learn

Apply Python confidently to infrastructure and operations tasks: Write clean, modular Python code using core principles, file handling, modules, and OOP.

Automate file-related operations: Efficiently manipulate, encrypt, and work with various file formats commonly used in DevOps, MLOps, and AIOps.

Create interactive command-line applications: Build CLIs with Python to automate tasks and streamline workflows.

Effectively manage Linux systems remotely: Use Python's Fabric library for remote execution and psutil for system monitoring

Create, manage, and publish Python packages: Organize code into reusable packages and distribute them on platforms like PyPI.

Utilize Docker for application deployments: Understand Docker image creation, containerization, and deployment.

Automate workflows with GitHub Actions: Design and configure CI/CD pipelines using GitHub Actions.

Implement CI/CD workflows utilizing AWS services: Design pipelines that leverage S3 for storage and EC2 instances for deployment.

Write tests specifically for MLOps projects: Ensure MLOps reliability and maintainability using Pytest.

Provision and manage infrastructure using code: Apply Infrastructure as Code (IaC) principles with Pulumi's Python SDK.

Experience a complete MLOps pipeline: Build an end-to-end MLOps solution integrating tools and concepts learned throughout the course.

Set up continuous monitoring for improved visibility: Implement monitoring and alerting using Prometheus and Grafana.

Requirements

No Programming Experience is needed

Just a Laptop and CLI to code

Description

Master the essential Python skills you need to streamline DevOps workflows, implement intelligent MLOps pipelines, and optimize AIOps practices. This comprehensive course dives into Python fundamentals, file automation, command-line mastery, Linux utilities, package management, Docker, CI/CD with AWS, infrastructure automation, and even advanced monitoring and logging techniques.Key Skills You'll Develop:Python Foundations: Get a robust understanding of variables, data types, control structures, functions, object-oriented programming, and best practices for clean Python code.File Automation: Effortlessly manipulate text, binary, and various file formats (like CSV, JSON, and more) used in MLOps, AIOps, and DevOps projects. Learn encryption strategies for secure file handling.Command-Line Power: Build command-line interfaces and automate tasks with Python libraries like argparse, Click, and fire.Linux Integration: Interact with Linux systems effectively using Python's Fabric and psutil libraries.Package Management: Learn to create, manage, and publish your own Python packages to streamline your workflows.Docker Expertise: Master Docker containerization for consistent and portable deployments.GitHub Actions Automation: Create and customize GitHub Actions workflows for your Python projects.AWS Essentials: Set up your AWS environment, work with S3 buckets, manage EC2 instances, and design CI/CD pipelines on AWS.Pytest Power: Write robust and maintainable tests for your MLOps projects using Pytest.Infrastructure as Code with Pulumi: Automate infrastructure provisioning and management using Pulumi's Python SDK.MLOps in Action: Participate in a hands-on demo showcasing a complete MLOps pipeline.Monitoring & Logging: Set up continuous monitoring with Prometheus and Grafana for actionable insights into your systems.Who This Course Is For:Developers interested in streamlining DevOps processesData scientists and ML engineers looking to enhance MLOps practicesIT professionals wanting to implement AIOps strategiesAnyone eager to master Python for infrastructure management and automation

Overview

Section 1: Introduction to the Course

Lecture 1 Welcome to the Course

Lecture 2 What makes this course Unique

Lecture 3 Source code access

Section 2: Python Essentials for DevOps - MLOps - AIOps

Lecture 4 Introduction to the Python

Lecture 5 Installing and Running Python

Lecture 6 Variables and Data Types in Python

Lecture 7 Jupyter Lab Interface Quick Tour

Lecture 8 Varaibles and Data Types - Hands On

Lecture 9 Comments in Python Programming Language

Lecture 10 Operators in Python Programming

Lecture 11 Operators in Python - Hands On

Lecture 12 Built-in Functions in Python Programming

Lecture 13 Built-in Functions in Python Programming - Hands On

Lecture 14 Built-in Functions in Python Programming - Part 2 - Hands On

Lecture 15 Sequences in Python

Lecture 16 Hands On Python Strings - Sequence Operations

Lecture 17 Hands On Python List - Sequence Operations

Lecture 18 Hands On Python Tuple - Sequence Operations

Lecture 19 Hands On Python Dictionary - Sequence Operations

Lecture 20 Hands On Python Sets - Sequence Operations

Lecture 21 Hands On Python Range - Sequence Operations

Lecture 22 Execution Control in Python

Lecture 23 Hands On – Conditional Statements in Python

Lecture 24 Hands On – For - Control Statements in Python

Lecture 25 Hands On – While - Control Statements in Python

Lecture 26 Hands On – Loop Control Statements in Python Programming

Lecture 27 Exception Handling in Python

Lecture 28 String Formatting in Python

Lecture 29 String Formatting - Hands On

Lecture 30 User Defined Functions in Python

Lecture 31 User Defined Functions & Scope of Variables Hands On

Lecture 32 Anonymous Functions - Lambda

Lecture 33 Advanced Functions - map, filter, list & dict comprehension

Lecture 34 Modules in Python

Lecture 35 Mudules in Python - Hands On

Lecture 36 Regular Expressions

Lecture 37 Regular Expressions Hands On

Lecture 38 Introduction to Object Oriented Python

Lecture 39 Hands On - Classes and Objects

Lecture 40 Object Oriented Concepts in Python

Lecture 41 Section Summary

Lecture 42 Object Oriented Concepts - Hands On

Section 3: Python File Automation - working with Files and Filesystem

Lecture 43 Introduction to Python File Automation

Lecture 44 Working with Files and Directory

Lecture 45 Working with Text files

Lecture 46 Working with Binary Files

Lecture 47 Working with Common File formats in DevOps - MLOps AIOps Projects

Lecture 48 Working with Common File formats in DevOps - MLOps AIOps Projects - Part 2

Lecture 49 Strategies for working with Large Files

Lecture 50 Encryption and Cryptography using Python

Lecture 51 Working with Directories in Python - os, shutil, pathlib

Lecture 52 Examples from MLOps

Section 4: Command Line Automation - DevOps - MLOps - AIOps

Lecture 53 Introduction to Working with Command Lines

Lecture 54 Working with sys module - Hands On

Lecture 55 Working with os module

Lecture 56 Working with subprocess module

Lecture 57 Working with Command Line tools

Lecture 58 sys.argv - command line inputs

Lecture 59 Argparse - Parsing Command Line inputs

Lecture 60 Function Decorators

Lecture 61 Parsing the Command line using Click

Lecture 62 Creating a More Complex CLI using Click

Lecture 63 Working with fire package

Section 5: Linux Utilities with Python

Lecture 64 Introduction to Python Fabric Library

Lecture 65 Hands On Python Fabric

Lecture 66 Monitor the System with psutil

Lecture 67 Hands On psutil

Section 6: Python Package Management

Lecture 68 Introduction to Python Package Management

Lecture 69 Hands on Package Management with Python

Lecture 70 Hands On MLOps Package to pypi

Section 7: Docker for DevOps - MLOps - AIOps

Lecture 71 Introduction to DevOps

Lecture 72 Introduction to Docker

Lecture 73 Docker Installation

Lecture 74 Docker Hands On

Section 8: Github Actions for Python Projects

Lecture 75 Introduction to GitHub Actions

Lecture 76 Quick Demo on github actions YAML file

Lecture 77 Understanding github Actions YAML file

Lecture 78 Create github Actions from Scratch

Lecture 79 Configure Workflow based on use case

Section 9: Getting Started with AWS - Prep work for CI CD Pipeline - Python Projects

Lecture 80 Agenda of the Section

Lecture 81 Create AWS Account

Lecture 82 Setting up MFA on Root Account

Lecture 83 Create IAM Account and Account Alias

Lecture 84 Setup CLI with Credentials

Lecture 85 IAM Policy

Lecture 86 IAM Policy generator & attachment

Lecture 87 Delete the IAM User

Lecture 88 S3 Bucket and Storage Classes

Lecture 89 Creation of S3 Bucket from Console

Lecture 90 Creation of S3 Bucket from CLI

Lecture 91 Version Enablement in S3

Lecture 92 Introduction EC2 instances

Lecture 93 Launch EC2 instance & SSH into EC2 Instances

Lecture 94 Clean Up Activity

Section 10: CI CD Pipeline with Github Actions - AWS EC2 Instances

Lecture 95 Agenda of the Section

Lecture 96 Exploring the files of CI CD Python

Lecture 97 Pre-requisite setup for ci cd pipeline

Lecture 98 Test the CI CD with AWS

Section 11: Pytest for MLOps - AIOps

Lecture 99 Introduction to Pytest

Lecture 100 pytest Hands on

Lecture 101 pytest fixtures

Section 12: Infrastructure Automation using Python

Lecture 102 Introduction to IAAC

Lecture 103 Introducing Pulumi

Lecture 104 Getting System rReady

Lecture 105 Pulumi Hands On

Lecture 106 Pulumi with Advanced Use case - EC2 with Security Group

Section 13: Python for MLOps - AIOps

Lecture 107 Introducing MLOps

Lecture 108 Hands On Demo MLOps

Lecture 109 Testing the MLOps

Section 14: Monitoring and Logging with Python

Lecture 110 Introduction to Continuous Monitoring

Lecture 111 Use case on Continuous Monitoring

Lecture 112 Introduction to Prometheus

Lecture 113 Architecture of Prometheus

Lecture 114 Metric Types of Prometheus

Lecture 115 Installation of Prometheus

Lecture 116 Introduction to Grafana

Lecture 117 Installation of Grafana

Lecture 118 Prometheus Configuration file

Lecture 119 Exploring the Basic Querying Prometheus

Lecture 120 Monitor the Infrastructure with Prometheus

Lecture 121 Monitor the Linux Server with Node Exporter

Lecture 122 Monitor the Client Application using Prometheus

Lecture 123 Monitor the FastAPI Application using Prometheus

Lecture 124 Monitor All EndPoints using Prometheus

Lecture 125 Create Visualization with Grafana

Lecture 126 Trigger Alerts with Grafana

Developers interested in streamlining DevOps processes,Data scientists and ML engineers looking to enhance MLOps practices,IT professionals wanting to implement AIOps strategies,Anyone eager to master Python for infrastructure management and automation