How can MLOps Add Value to Computer Vision Projects in 2024?

Computer vision is a field of ​​artificial intelligence that involves using computer systems and algorithms to extract meaningful information from images, videos, and other visual data. It enables computer systems to perform tasks such as object recognition, flaw detection, and quality control specific to the human brain. 

Computer vision is currently used in areas such as manufacturing, healthcare, military, automotive, and security and enables applications such as autonomous vehicles and more accurate disease detection.

Figure 1: Object detection of computer vision

High-performing computer vision algorithms require large amounts of visual data that are labeled accurately. Collecting and accurately labeling such data is expensive and time-consuming which makes errors costly. Therefore, businesses should follow a systematic approach to computer vision development. MLOps practices can offer the required systematicity to CV projects.

Benefits of MLOps for Computer Vision

Figure 2: Computer vision pipeline

Creating a well-functioning computer vision system requires creating a pipeline that covers the entire computer vision development lifecycle. As seen in Figure 2, an end-to-end computer vision pipeline encompasses:

• Data management including data collection, cleaning, labeling, and selection
• Model creation
• Model training
• Model deployment
• Model monitoring

These processes are what MLOps aims to automate and streamline. 

MLOps for computer vision sometimes referred to as CVOps, can:

• Standardize data management processes to ensure CV models are constantly fed with high-quality data.
• Enable testing while preserving the working versions of datasets, models, features, or hyperparameters with version control.
• Apply continuous integration and delivery. With CI/CD all data flows can be automated and orchestrated from initial sources to final deployment. 
• Apply continuous training to ensure that models are retrained constantly.
• Monitor the computer vision models after deployment.

These benefits enable businesses to streamline the development of computer vision systems and to ensure that models are reliable and give healthy outputs.

If you want to get started with MLOps in your computer vision project, you can check our data-driven list of MLOps platforms. If you have any other questions, please don’t hesitate to contact us: