Dive into MLOPS basics to improve your skills for designing, developing, and deploying computer vision projects for real-world, industrial applications
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These days, we encounter (and maybe 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 not a big deal. But what is the correct way of creating and deploying an AI project for industrial usage?
MLOps (Machine Learning Operations) is a set of practices, tools, and frameworks aimed at automating the development, 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 both stages.
In this complete set of tutorials, we will be covering each step of a computer vision project’s MLOPS cycle.
A complete cycle of MLOPS for an AI project is listed below, with an example tool that we will use to accomplish the related step:
- Data versioning & Management (DVC)
- Experiment Tracking (MLFlow)