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Computer Vision Consulting in 2024: Benefits & Vendor Selection

The increasing use of computer vision (CV) in industries ranging from manufacturing and automotive to healthcare and security is driving a growth in the CV software and hardware market which is expected to reach $48.6 Billion by 2022. The ability to derive meaningful information from a large amount of visual data such as images and videos enables applications such as self-driving cars and highly accurate disease detection.

However, applying computer vision techniques to businesses problems has its own challenges. Getting consulting services from a computer vision consultant can help businesses overcome these challenges and reduce the chance of failed projects.

What are the challenges of computer vision applications?

Some of the challenges that face computer vision adoption include:

Hardware requirements

Computer vision applications involve hardware systems such as cameras or IoT devices, and their algorithms require a considerable amount of processing power. These factors imply that investing in proper hardware is a must for a successful computer vision application, especially for applications involving real-time computation.

Cloud services such as GCS, AWS, or Azure are more scalable alternatives to building on-premises hardware systems in terms of cost. However, using cloud services for computer vision applications is challenging in the case of real-time computation. Images or videos need to be sent to the cloud before processing which can boost the cost of the service.

Data collection and processing

Collecting and accurately labeling visual data is essential for most computer vision problems. If there are no public datasets that you can use in your computer vision problem, you need to collect and annotate the data. This is a challenge because:

  • Privacy: Collecting sufficient image or video data in most business applications requires access to sensitive customer data. For instance, collecting and processing CT scans of patients can raise privacy concerns and there are regulations such as GDPR or HIPAA that limit businesses from collecting and storing such data.
  • High costs: Collecting visual data can be costly. For example, collecting data that represents the diversity of real-world road events for an autonomous vehicle can be prohibitively expensive.
  • Annotation efforts: Labeling images or videos is a labor-intensive and time-consuming task.

What are the benefits of getting computer vision consulting services?

  • Access to expertise: Hiring and training data science staff and providing the necessary resources to them is costly. Getting consulting services enables businesses to find and choose experts based on the necessities of their specific computer vision application.
  • Reduced development time and cost: Getting consulting services from a consultant that specializes in computer vision applications can speed up the development and deployment process. There is a good chance that they have already built similar projects and have the necessary experience and resources in hand. This saves businesses from endless experimenting and investing in expensive hardware.
  • Better data management: As we mentioned above, collecting and labeling images or videos for predictive models is challenging. Consultants would have the necessary experience to mine and process the raw visual data to make it suitable and efficient for your computer vision project. They can also leverage appropriate privacy-enhancing technologies (PETs) to make sure the security of your data and the privacy of your customers.

What to consider when choosing a computer vision consultant?

  • Domain expertise: The vendor should have the necessary experience with computer vision projects relevant to your business’ industry or business objective and has expertise in relevant technologies. You should look for knowledge in technologies such as OCR, NLP, and convolutional neural networks (CNN) and experience with software such as TensorFlow, OpenCV, and SimpleCV.
  • Available hardware: You should pay attention to the equipment and hardware the vendor possesses.
  • References: References are a good source of information. A reputable vendor would have a portfolio to document its successfully completed projects.
  • TCO: Cost to build, deploy and maintain the model should be considered.
  • Compliance: You should discuss how the vendor ensures compliance with regulations such as GDPR, HIPAA etc.
  • Support model: Model accuracies degrade over time and the vendor should be available for model improvement if model accuracy degrades.

For more on consulting in AI and ML, you can check our articles on:

You can also check our sortable/filterable lists of AI consultants and data science consultants.

If you still have questions about consulting in computer vision, we can help:

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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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