Complete EdX Learning Path 2024

Welcome to the comprehensive 2024 learning path from Pragmatic AI Labs on edX. This carefully curated collection of courses will take you from foundational concepts to advanced implementations across cloud computing, AI, and MLOps.

🌟 Azure (2 courses)

• Azure Fundamentals
• End-to-End LLM with Azure

🔐 AWS (1 course)

• Authoritative AWS

☁️ Cloud Computing (2 courses)

• Cloud Computing Foundations
• Cloud Virtualization, Containers and APIs

💻 Computer Science (27 courses)

• Introduction to Generative AI
• Rust Fundamentals
• Generative AI and LLMs on AWS
• Rust Data Engineering
• Data Engineering with Databricks
• Open Source LLMOps
• Rust for Machine Learning Operations (LLMOps)
• Applied Local Large Language Models
• Azure AI Fundamentals
• Python Fundamentals for MLOps
• DevOps, DataOps, MLOps
• Introduction to LLM Vulnerabilities
• Beginning Llamafile
• MLOps Platforms: Amazon SageMaker and Azure ML
• MLOps Tools: MLflow and Hugging Face
• Python and Rust with Linux Command Line Tools
• Rust for DevOps
• Rust GUI Development for Linux
• Applied GitHub Platform
• Authoritative GCP
• Interactive Python Standard Library
• GitHub Enterprise
• LLM Server
• Rust-Powered AWS Serverless
• Radical Ideas AI Ethics
• Advanced RAG

📢 Communication (1 course)

• Building AI with Bedrock Agent

📊 Data Engineering (6 courses)

• Python and Pandas for Data Engineering
• Scripting with Python and SQL for Data Engineering
• Virtualization, Docker, and Kubernetes for Data Engineering
• Linux and Bash for Data Engineering
• Cloud Data Engineering
• Web Applications and Command Line Tools for Data Engineering

🤖 Machine Learning (1 course)

• Cloud Machine Learning Engineering and MLOps

🗣️ Natural Language Processing (1 course)

• Small Language Models

🧠 Artificial Intelligence (2 courses)

• Agile with AI
• Using GenAI to Automate Software Development Tasks

This learning path is designed for everyone, from beginners to experienced professionals. Each course builds upon previous knowledge while providing practical, hands-on experience. The curriculum emphasizes both theoretical understanding and practical implementation, ensuring you're well-prepared for real-world challenges in AI, cloud computing, and software development.

Recommended Courses

Based on this article's content, here are some courses that might interest you:

1. Enterprise AI Operations with AWS (2 weeks)
   Master enterprise AI operations with AWS services

2. AWS Advanced AI Engineering (1 week)
   Production LLM architecture patterns using Rust, AWS, and Bedrock.

3. DevOps, DataOps, and MLOps (5 weeks)
   Learn to build and deploy production-ready machine learning systems using modern DevOps and MLOps practices. Master essential tools and frameworks while implementing end-to-end ML pipelines.

4. DevOps, DataOps, and MLOps (5 weeks)
   Learn to build and deploy production-ready machine learning systems using modern DevOps and MLOps practices. Master essential tools and frameworks while implementing end-to-end ML pipelines.

5. MLOps Platforms: Amazon SageMaker and Azure ML (5 weeks)
   Learn to implement end-to-end MLOps workflows using Amazon SageMaker and Azure ML services. Master the essential skills needed to build, deploy, and manage machine learning models in production environments across multiple cloud platforms.

Learn more at Pragmatic AI Labs