Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.
DevOps, DataOps, MLOps
DevOps, DataOps, MLOps
This course is part of MLOps | Machine Learning Operations Specialization
Taught in English
Some content may not be translated
Instructors: Noah Gift
6,680 already enrolled
Included with 
Course
Gain insight into a topic and learn the fundamentals
3.7
(43 reviews)
Advanced level
Recommended experience
45 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
Build operations pipelines using DevOps, DataOps, and MLOps
Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)
Build and deploy machine learning models in a production environment using MLOps tools and platforms.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
13 quizzes
Course
Gain insight into a topic and learn the fundamentals
3.7
(43 reviews)
Advanced level
Recommended experience
45 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the MLOps | Machine Learning Operations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 5 modules in this course
This week you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.
What's included
22 videos10 readings4 quizzes2 discussion prompts1 ungraded lab
This week you will learn how to apply essential skills in math and data science for MLOps and apply it by building simulations.
What's included
5 videos9 readings3 quizzes3 ungraded labs
This week you will learn how to build operations pipelines and then apply these skills by building solutions for pre-trained Hugging Face models.
What's included
20 videos9 readings1 quiz2 ungraded labs
This week you will learn how to build end to end MLOps and AIOps solutions and apply it by building solutions with pre-trained models from OpenAI while benefiting from using AI Pair Programming tools like GitHub Copilot.
What's included
12 videos9 readings1 quiz2 ungraded labs
This week, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This week will cover various practical applications of Rust, such as CLI, Web, and MLOps solutions, as well as cloud computing solutions for AWS, GCP, and Azure. You'll also learn how to build Rust solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and Machine Learning Operations (MLOps). By the end of this week, you will have a strong understanding of Rust's key syntax and features, and be able to leverage Rust for GPU-accelerated machine learning tasks.
What's included
25 videos11 readings4 quizzes3 ungraded labs
Instructors
Instructor ratings
3.6 (16 ratings)Offered by
Recommended if you're interested in Machine Learning
Duke University
Duke University
Duke University
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