MLOps | Machine Learning Operations Specialization

What you'll learn

Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Skills you'll gain

Data Analysis
Responsible AI
Cloud Deployment
DevOps
Cloud Computing
Fine-tuning
Big Data
Data Management
MLOps (Machine Learning Operations)
Unit Testing
Machine Learning

Tools you'll learn

GitHub Copilot
Hugging Face
Microsoft Azure
GitHub
AWS SageMaker
NumPy
Model Deployment
Python Programming
Pandas (Python Package)

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Duke University

Specialization

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Applied Learning Project

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

Building a Python script to automate data preprocessing and feature extraction for machine learning models.
Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.