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Python Programming for MLOps – AIOps – DevOps

Name: Python Programming for MLOps - AIOps - DevOps
Brand: Udemy

June 25, 2024 Development, Programming, Udemy CoursesUdemy

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Python Programming for MLOps - AIOps - DevOps

Optimize MLOps, AIOps, and DevOps Workflows with Python

Product Brand: Udemy

Editor's Rating:
4.5

Udemy Coupon Code for Python Programming for MLOps – AIOps – DevOps Course. Optimize MLOps, AIOps, and DevOps Workflows with Python

Created by Manifold AI Learning | 17 hours on-demand video course

Python Programming Course Overview

Python Programming for MLOps – AIOps – DevOps
What you’ll learn
Recommended MLOps Course
Who this course is for
Best Python Programming Course for 2024

Python Programming for MLOps – AIOps – DevOps

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.

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.

Recommended MLOps Course

Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

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Master the theory, practice, and math behind ML,NLP. Build real-world projects with MLOPS, git, Dockers with deployment

Are you looking to master Machine Learning (ML) and Natural Language Processing (NLP) from the ground up? This Complete Machine Learning,NLP Bootcamp MLOPS & Deployment course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.

By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.

MLOps. Machine Learning deployment: AWS, GCP & Apple in<7hrs

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Learn to deploy ML models to multiple environments, Data Engineering best practices and BOOST your career

MLOps has been helping me automate & roll out robust, easily maintainable and state-of-the-art ML in IT, Food and Travel industries over the last 6 years. With the help of modern Cloud Computing and open source software I’ve brought live dozens of ML research projects, successfully solved very complex Business challenges and even changed the country where I live & work!

The MLOps. Machine Learning deployment: AWS, GCP & Apple in<7hrs course will also teach you about Data Drifts: a common issue arising in the world of Machine Learning models. We will learn what these are, how to discover them in a timely manner and what actions to take to mitigate their effect on model’s performance.