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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

Source: Cameralyze

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

Source: Devopedia

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:

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:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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