Online Course – Certified Professional Internship in Machine Learning Operations from Duke University

Internship – 4-part course series

This intensive course series is designed for people with programming knowledge, such as developers, data scientists, and researchers. You’ll learn important MLOps skills, including using Python and Rust, leveraging GitHub Copilot for increased productivity, and using platforms like Amazon SageMaker, Azure ML, and MLflow. You’ll also learn how to fine-tune large language models (LLMs) using Hugging Face, and learn about deploying robust and efficient integrated binary models in ONNX format, preparing you for success in the emerging field of MLOps.

Career paths

Through this series of courses, you will begin to acquire skills for a variety of career paths:

• Data Science – Analyzing and understanding complex data sets, developing machine learning models, implementing data management, and advancing data-driven decision-making.
• Machine Learning Engineering – Designing, building, and deploying machine learning models and systems to solve real-world problems.
• Cloud ML Solutions Architect – Utilizing cloud platforms like AWS and Azure to design and manage ML solutions flexibly and cost-effectively.
• Artificial Intelligence (AI) Product Management – Bridging the gap between business, engineering, and data science teams to deliver meaningful AI/ML products.

Hands-on Learning Project

Explore and practice your MLOps skills with hands-on exercises and Github platforms.

• Building a Python script to automate data processing and feature extraction for machine learning models.
• Developing a real-world ML/AI solution using AI-based pair programming and GitHub Copilot, demonstrating your ability to collaborate with AI.
• Creating web applications and command-line tools to interact with machine learning models using Gradio, Hugging Face, and the Click framework.
• Implementing GPU-accelerated machine learning tasks using Rust to improve performance and efficiency.
• Train, optimize, and deploy machine learning models on Amazon SageMaker and Azure ML for cloud-based MLOps.
• Full MLOps pipeline design with MLflow, project management, modeling, and system feature tracking.
• Configure and deploy large language models (LLMs) and patient models using ONNX format with Hugging Face. Create interactive demos to effectively demonstrate your work and progress.