Duke University - MLOps | Machine Learning Operations Specialization

Offered byCoursera

MLOps | Machine Learning Operations Specialization
at
Coursera
Overview

Become a Machine Learning Engineer. Level-up your programming skills with MLOps

Duration	6 months
Start from	Start Now
Total fee	Free
Mode of learning	Online
Official Website	Explore Free Course
Credential	Certificate

MLOps | Machine Learning Operations Specialization
at
Coursera
Highlights

Earn a Certificate upon completion
Skills -Data Management,Devops,MLOps,Machine Learning,Github,Python Programming,Data Analysis,Microsoft Azure,Big Data,Amazon Web Services (Amazon AWS),Cloud Computing,Rust Programming
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. 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.

MLOps | Machine Learning Operations Specialization
at
Coursera
Course details

Skills you will learn

Python Embedded systems Microsoft Azure Data Science

More about this course

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
Through this series, you will begin to learn skills for various career paths:
1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
Applied Learning Project
Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. 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.