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
Instructors: Noah Gift
23,025 already enrolled
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4.2
(118 reviews)
Advanced level
Recommended experience
Flexible schedule
Approx. 44 hours
Learn at your own pace
93%
Most learners liked this course
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 assignments
Taught in English
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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.
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There are 5 modules in this course
In this module, 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 assignments2 discussion prompts1 ungraded lab
In this module, 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 assignments3 ungraded labs
In this module, 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 assignment2 ungraded labs
In this module, 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 assignment2 ungraded labs
In this module, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This module 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 module, 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 assignments3 ungraded labs
Instructors
Instructor ratings
4.3 (43 ratings)Offered by
Recommended if you're interested in Machine Learning
Duke University
Duke University
Duke University
Duke University
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4.2
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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