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:
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Python Foundations: Get a robust understanding of variables, data types, control structures, functions, object-oriented programming, and best practices for clean Python code.
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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.
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Command-Line Power: Build command-line interfaces and automate tasks with Python libraries like argparse, Click, and fire.
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Linux Integration: Interact with Linux systems effectively using Python’s Fabric and psutil libraries.
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Package Management: Learn to create, manage, and publish your own Python packages to streamline your workflows.
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Docker Expertise: Master Docker containerization for consistent and portable deployments.
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GitHub Actions Automation: Create and customize GitHub Actions workflows for your Python projects.
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AWS Essentials: Set up your AWS environment, work with S3 buckets, manage EC2 instances, and design CI/CD pipelines on AWS.
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Pytest Power: Write robust and maintainable tests for your MLOps projects using Pytest.
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Infrastructure as Code with Pulumi: Automate infrastructure provisioning and management using Pulumi’s Python SDK.
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MLOps in Action: Participate in a hands-on demo showcasing a complete MLOps pipeline.
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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
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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
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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