MLOps

The MLOps Specialization | Machine Learning Operations is a comprehensive, career-oriented program designed to help learners move from experimental machine learning to production-grade ML systems.

You will gain hands-on MLOps skills using Python, Rust, GitHub Copilot, SageMaker, Azure ML, MLflow, and Hugging Face to build, deploy, and manage real-world AI solutions.

Hands-On Learning & Applied Projects

Throughout the specialization, learners work on practical, real-world MLOps projects, supported by GitHub repositories and live mentoring.

You will:

• Automate data preprocessing and feature engineering with Python
• Develop a real ML/AI solution using pair programming and GitHub Copilot
• Build web applications and CLI tools using Gradio, Hugging Face, and Click
• Implement GPU-accelerated ML tasks using Rust
• Train, optimize, and deploy ML models on Amazon SageMaker and Azure ML
• Design a complete MLOps pipeline with MLflow
• Fine-tune and deploy LLMs and ONNX-based models
• Create interactive demos to showcase production-ready ML solutions

Program Structure (Blended Learning Format)

Online Learning – Duke University

The online component, developed by Duke University, consists of 4 in-depth courses covering the theoretical foundations and practical tools of MLOps.

🔹 Course 1 – Python Essentials for MLOps

You develop clean, testable, and production-ready Python code, not just experimental scripts or notebooks.

🔹 Course 2 – DevOps, DataOps, and MLOps Pipelines

You gain hands-on experience with how ML systems are deployed and managed in real-world enterprise environments.

🔹 Course 3 – Data Science, Cloud ML, and Production Deployment

You learn to combine data science and cloud technologies to deliver scalable ML solutions.

🔹 Course 4 – Advanced MLOps with MLflow and Hugging Face

You gain practical experience deploying modern ML models and large language models (LLMs) in production environments.

• Formateurs (Université MUST) : Nos professeurs de l'université MUST sont des leaders académiques et des experts en MLOps, qui apportent au programme d'études un mélange de connaissances théoriques et d'applications concrètes.
• Enseignants (Université Duke) : Le programme fait également appel à des formateurs de haut niveau issus de l'université Duke, réputés pour leurs recherches avancées et leur expertise pratique en MLOps, qui abordent les concepts clés et les meilleures pratiques du secteur.