Courses
MLOps Concepts
中间的技能水平
更新 2025年12月TheoryMachine Learning2小时16 videos46 Exercises2,950 XP39,698成就声明
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试试DataCamp for Business课程描述
Learn about Machine Learning Operations (MLOps)
Understanding MLOps concepts is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production.In this course, you’ll learn what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. After learning about the essential MLOps concepts, you’ll be well-equipped in your journey to implement machine learning continuously, reliably, and efficiently.
Discover How Machine Learning Can be Scaled and Automated
How can we scale our machine learning projects using the minimum time and resources? And how can we automate our processes to reduce the need for manual intervention and improve model performance? These are fundamental Machine Learning questions that MLOps provides the answers to.In this MLOps course, you’ll start by exploring the basics of MLOps, looking at the core features and associated roles. Next, you’ll explore the various phases of the machine learning lifecycle in more detail.
As you progress, you'll also learn about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. You’ll explore key MLOps concepts, giving you a firmer understanding of their applications.
先决条件
Understanding Machine LearningUnderstanding Data Engineering1
Introduction to MLOps
First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.
2
Design and Development
Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.
3
Deploying Machine Learning into Production
In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.
4
Maintaining Machine Learning in Production
Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.