From the course: MLOps and Data Pipeline Orchestration for AI Systems
Join today to access over 24,700 courses taught by industry experts.
Tracking artifacts in MLOps
From the course: MLOps and Data Pipeline Orchestration for AI Systems
Tracking artifacts in MLOps
- [Instructor] In software development and DevOps, there's just one artifact that you have to track, and that is the code that you deploy. In MLOps, on the other hand, you have to track multiple artifacts. Let's see what they are. The first thing is of course code. This involves versioning and managing the code base used for developing, training, and deploying machine learning models, ensuring reproducibility and collaboration through tools like Git. Another artifact is the model itself. This is where you focus on managing the lifecycle of machine learning models, including versioning, storing different iterations, tracking performance metrics, and managing the models deployment and transitions to production. And then you have to track data. This encompasses versioning and monitoring the data sets used for training and evaluating machine learning models, ensuring data lineage, reproducibility of experiments, and detection of data drift or quality issues. Let's talk about why you…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.