Delve into the basics of MLOPS to improve your skills in designing, developing, and implementing computer vision projects for real-world industrial applications.
Nowadays, we encounter (and perhaps produce on our own) many computer vision projects, where ai is the hottest topic for new technologies. Fine-tuning a pre-trained image classification, object detection, or any other computer vision project is no big deal. But what is the right way to create and implement an ai project for industrial use?
MLOps (Machine Learning Operations) is a set of practices, tools and frameworks intended to automate the development, deployment, monitoring and management of machine learning models in production environments. It bridges the gap between research and development environments and helps us improve both stages.
In this complete set of tutorials, we will cover each step of the MLOPS cycle of a computer vision project.
A complete MLOPS cycle for an ai project is listed below, with an example tool that we will use to perform the related step:
- Data versioning and management (DVC)
- Experiment tracking (MLFlow)
