Edge AI Lifecycle

Collecting data, analyzing the data, training a machine learning (ML) model, and deploying that model often require many steps and iterations to find a solution that meets your needs. This edge AI lifecycle is often a team effort and requires juggling various considerations as you develop your solution. We take a look at those considerations and examine each step in the lifecycle to help you understand what goes into an edge AI project.

Model deployment can be tricky, as it requires loading the model onto your end device and creating an application around that model. Once deployed, you will need to monitor the model for accuracy, biases, and potential drift, which often results in collecting additional data. The process of automating this pipeline is known as machine learning operations (MLOps), which is covered in the next section.

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

Chapters:
0:00 Edge AI example
1:09 Project considerations
2:30 Data collection and analysis
4:30 Data cleaning
5:10 Feature extraction
6:18 Model training and testing
7:09 Model deployment
8:20 Machine learning pipeline
8:52 Model biases and drift
11:12 Operations and maintenance