Python Programming for MLOps – Production Environment

Python Programming for MLOps – Production Environment, available at $54.99, has an average rating of 4.68, with 127 lectures, 5 quizzes, based on 15 reviews, and has 282 subscribers.

You will learn about 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. This course is ideal for individuals who are Developers interested in streamlining DevOps processes or Data scientists and ML engineers looking to enhance MLOps practices or IT professionals wanting to implement AIOps strategies or Anyone eager to master Python for infrastructure management and automation It is particularly useful for Developers interested in streamlining DevOps processes or Data scientists and ML engineers looking to enhance MLOps practices or IT professionals wanting to implement AIOps strategies or Anyone eager to master Python for infrastructure management and automation.

Enroll now: Python Programming for MLOps – Production Environment

Summary

Title: Python Programming for MLOps – Production Environment

Price: $54.99

Average Rating: 4.68

Number of Lectures: 127

Number of Quizzes: 5

Number of Published Lectures: 127

Number of Published Quizzes: 5

Number of Curriculum Items: 132

Number of Published Curriculum Objects: 132

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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 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

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Welcome to the Course

Lecture 2: What makes this course Unique

Lecture 3: Source code access

Chapter 2: Python Essentials for DevOps – MLOps – AIOps

Lecture 1: Introduction to the Python

Lecture 2: Installing and Running Python

Lecture 3: Variables and Data Types in Python

Lecture 4: Jupyter Lab Interface Quick Tour

Lecture 5: Varaibles and Data Types – Hands On

Lecture 6: Comments in Python Programming Language

Lecture 7: Operators in Python Programming

Lecture 8: Operators in Python – Hands On

Lecture 9: Built-in Functions in Python Programming

Lecture 10: Built-in Functions in Python Programming – Hands On

Lecture 11: Built-in Functions in Python Programming – Part 2 – Hands On

Lecture 12: Sequences in Python

Lecture 13: Hands On Python Strings – Sequence Operations

Lecture 14: Hands On Python List – Sequence Operations

Lecture 15: Hands On Python Tuple – Sequence Operations

Lecture 16: Hands On Python Dictionary – Sequence Operations

Lecture 17: Hands On Python Sets – Sequence Operations

Lecture 18: Hands On Python Range – Sequence Operations

Lecture 19: Execution Control in Python

Lecture 20: Hands On – Conditional Statements in Python

Lecture 21: Hands On – For – Control Statements in Python

Lecture 22: Hands On – While – Control Statements in Python

Lecture 23: Hands On – Loop Control Statements in Python Programming

Lecture 24: Exception Handling in Python

Lecture 25: String Formatting in Python

Lecture 26: String Formatting – Hands On

Lecture 27: User Defined Functions in Python

Lecture 28: User Defined Functions & Scope of Variables Hands On

Lecture 29: Anonymous Functions – Lambda

Lecture 30: Advanced Functions – map, filter, list & dict comprehension

Lecture 31: Modules in Python

Lecture 32: Mudules in Python – Hands On

Lecture 33: Regular Expressions

Lecture 34: Regular Expressions Hands On

Lecture 35: Introduction to Object Oriented Python

Lecture 36: Hands On – Classes and Objects

Lecture 37: Object Oriented Concepts in Python

Lecture 38: Section Summary

Lecture 39: Object Oriented Concepts – Hands On

Chapter 3: Python File Automation – working with Files and Filesystem

Lecture 1: Introduction to Python File Automation

Lecture 2: Working with Files and Directory

Lecture 3: Working with Text files

Lecture 4: Working with Binary Files

Lecture 5: Working with Common File formats in DevOps – MLOps AIOps Projects

Lecture 6: Working with Common File formats in DevOps – MLOps AIOps Projects – Part 2

Lecture 7: Strategies for working with Large Files

Lecture 8: Encryption and Cryptography using Python

Lecture 9: Working with Directories in Python – os, shutil, pathlib

Lecture 10: Examples from MLOps

Chapter 4: Command Line Automation – DevOps – MLOps – AIOps

Lecture 1: Introduction to Working with Command Lines

Lecture 2: Working with sys module – Hands On

Lecture 3: Working with os module

Lecture 4: Working with subprocess module

Lecture 5: Working with Command Line tools

Lecture 6: sys.argv – command line inputs

Lecture 7: Argparse – Parsing Command Line inputs

Lecture 8: Function Decorators

Lecture 9: Parsing the Command line using Click

Lecture 10: Creating a More Complex CLI using Click

Lecture 11: Working with fire package

Chapter 5: Linux Utilities with Python

Lecture 1: Introduction to Python Fabric Library

Lecture 2: Hands On Python Fabric

Lecture 3: Monitor the System with psutil

Lecture 4: Hands On psutil

Chapter 6: Python Package Management

Lecture 1: Introduction to Python Package Management

Lecture 2: Hands on Package Management with Python

Lecture 3: Hands On MLOps Package to pypi

Chapter 7: Docker for DevOps – MLOps – AIOps

Lecture 1: Introduction to DevOps

Lecture 2: Introduction to Docker

Lecture 3: Docker Installation

Lecture 4: Docker Hands On

Chapter 8: Github Actions for Python Projects

Lecture 1: Introduction to GitHub Actions

Lecture 2: Quick Demo on github actions YAML file

Lecture 3: Understanding github Actions YAML file

Lecture 4: Create github Actions from Scratch

Lecture 5: Configure Workflow based on use case

Chapter 9: Getting Started with AWS – Prep work for CI CD Pipeline – Python Projects

Lecture 1: Agenda of the Section

Lecture 2: Create AWS Account

Lecture 3: Setting up MFA on Root Account

Lecture 4: Create IAM Account and Account Alias

Lecture 5: Setup CLI with Credentials

Lecture 6: IAM Policy

Lecture 7: IAM Policy generator & attachment

Instructors

Manifold AI Learning ®
Learn the Future – Data Science, Machine Learning & AI

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 10 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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