Python Programming for MLOps – Production Environment
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Python Programming for MLOps - Production Environment
Optimize MLOps, AIOps, and DevOps Workflows with Python - Essential skills for productionalization
Product Brand: Udemy
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Python Programming for MLOps – Production Environment Coupon Code. Optimize MLOps, AIOps, and DevOps Workflows with Python – Essential skills for productionalization
Python Programming for MLOps – Production Environment
Python Programming for MLOps – Production Environment Course. 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.
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.
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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
Taught by Manifold AI Learning
