Dive into MLOPS basics to enhance your skills for designing, developing, and deploying 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 most well liked topic for brand new technologies. High-quality-tuning a pre-trained image classification, object detection, or some other computer vision project just isn’t a giant deal. But what’s the right way of making and deploying an AI project for industrial usage?
MLOps (Machine Learning Operations) is a set of practices, tools, and frameworks geared toward automating the event, deployment, monitoring, and management of machine learning models in production environments. It bridges the gap between the research and development environments and helps us improve each stages.
In this whole set of tutorials, we shall be covering each step of a pc vision project’s MLOPS cycle.
An entire cycle of MLOPS for an AI project is listed below, with an example tool that we are going to use to perform the related step:
- Data versioning & Management (DVC)
- Experiment Tracking (MLFlow)
