What is Edge MLOps?

Edge machine learning operations (MLOps) is the application of tools and techniques to ensure repeatability and compliance of model development and deployment to edge computing devices. It involves building version control and automation systems along with employing best practices to meet various laws and regulations. As MLOps is a team effort, such adherence to these practices will increase team cooperation and productivity.

Almost every aspect of machine learning (ML) can be automated, from data collection to model testing to deployment. Edge MLOps offers additional difficulties, as it involves deploying models to edge devices rather than cloud servers. Monitoring model performance for data and concept drift also becomes important. As the operating environment changes, the model may need to be updated, and having a fleshed-out MLOps system can decrease the burden on engineering teams.

You can read more about edge MLOps and take a practice quiz here: https://docs.edgeimpulse.com/docs/con...

Chapters:
0:00 Overview of DevRel and MLOps
1:49 Benefits of MLOps
2:16 Review of edge AI lifecycle
4:01 Version control
5:15 Automation
7:24 Automatic updates
9:37 Model drift
11:32 Governance
12:32 Information security
12:54 Examples of MLOps tools