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  • Computer Vision Managed Services – Why and Why Now?

Computer Vision Managed Services – Why and Why Now?

Introduction

Managed services refer to outsourcing various IT operations and functions to a third-party managed services provider (MSP). For computer vision, this involves an MSP taking care of deploying, hosting, and managing computer vision solutions on behalf of the client.

Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs – and take actions or make recommendations based on that information. Computer vision powers use cases like barcode and text capture, ID documents capture, facial recognition, defect detection in manufacturing, analyzing medical images, visual controls, and much more.

In recent years, managed computer vision services have been gaining popularity due to various factors:

    Computer vision requires significant investments in infrastructure like powerful GPUs and specialized hardware. Managed services allow organizations to avoid large upfront costs.

    Developing and maintaining an in-house computer vision capability requires specialized expertise in computer vision, deep learning and related domains. Many organizations lack this specialized talent.

    Computer vision models need to be constantly improved and iterated as new algorithms and techniques emerge. Keeping models updated is challenging without dedicated staff.

    Data labeling, annotation and model training are time consuming tasks. MSPs can handle this heavy lifting more efficiently.

    MSPs can achieve economies of scale by leveraging infrastructure and talent across clients. This allows them to offer computer vision capabilities that may be unfeasible in-house.

    Rapid innovation is happening in computer vision. MSPs allow clients to take advantage without incurring risks or disruptions involved in adopting new technologies.

    MSPs handle reliability, scaling, security, compliance and other aspects, allowing clients to focus on their core business.

    Managed services enable even small and mid-sized organizations to leverage advanced computer vision capabilities without large investments. The scalability, cost efficiency and low risk offered by managed services make them an attractive option as computer vision adoption grows.

    Benefits of Managed Computer Vision Services

    Some of the key benefits of using a managed computer vision provider include:

      Access to Computer Vision Expertise Managed service providers employ teams of computer vision scientists, engineers, and developers. This level of expertise is difficult and expensive to build internally, especially for smaller organizations. With a managed service, companies can tap into these skills on-demand.

      Cost Savings Building an in-house computer vision team requires hiring scarce and expensive talent. There are also costs for computing infrastructure and ongoing research and development. A managed service simplifies budgeting by providing computer vision capabilities as a pay-as-you-go operating expense.

      Quick Time to Market Getting an in-house computer vision system deployed can take months or longer. Managed services have pre-built capabilities that can be implemented in weeks or even days. The faster time-to-value makes it easier to justify the investment.

      By leveraging a managed service provider, organizations can quickly and cost-effectively integrate computer vision into their products and processes without large upfront investments. The on-demand access to skilled talent and infrastructure removes common barriers to adopting these transformative technologies.

      Use Cases

      Computer vision is being applied in several key areas to drive business value:

      Self-checkout

      Computer vision enables cashier-less checkout through object recognition. Cameras identify items as customers pick them up, then charge their account when they leave the store. This improves customer experience with faster checkout and reduces labor costs.

      OCR and Text Recognition

      Optical Character Recognition (OCR) and text recognition technology facilitate seamless self-service in various transactions. Application use smartphone camera and convert printed or handwritten text into digital data, enabling instant processing and validation of infromation. This streamlines the customer journey by expediting data entry and minimizes staffing needs for data verification.

      Inventory tracking

      Computer vision tracks inventory and assets through visual identification and counting. It can track stock levels, find misplaced items, and detect theft or loss. This provides real-time inventory visibility and prevents out of stocks or shrinkage.

      Quality control and defect detection

      Computer vision performs automated visual inspections to identify defects, damage, and anomalies in products or parts on production and assembly lines. This enables quality control without slow, manual human inspections. Computer vision with deep learning can be highly accurate in spotting defects, reducing waste and improving quality.

      Manufacturing automation

      Computer vision guides robots to pick, move, and assemble parts accurately on production lines. It provides visual feedback for precise operation despite variances. This improves automation, efficiency and quality in manufacturing.

      Challenges of In-House Computer Vision

      Building an in-house computer vision capability can be extremely challenging for most organizations. Here are some of the key difficulties:

      Recruiting and Retaining Talent

      Finding and hiring top-notch computer vision engineers is very difficult and expensive. These skills are in extremely high demand at big tech firms, so attracting and retaining this talent requires significant resources and effort. Most companies simply cannot compete when it comes to compensation and benefits.

      Long Development Cycles

      It takes a great deal of time and effort to develop custom computer vision models from scratch. Data needs to be collected and cleaned, algorithms tested and refined, models trained and validated, and the final system integrated into applications. This process can easily take 6 months to a year even for an experienced team.

      Maintenance and Updates

      Computer vision systems need to be constantly monitored, refined, and updated to maintain accuracy over time. As conditions change, models degrade and need retraining. In-house teams need to allocate engineering resources to continuously manage and upgrade the system.

      Building an internal computer vision capability makes sense for certain organizations, but for most companies it is too costly and resource-intensive compared to leveraging managed services. Computer vision is extremely complex and specialized – it is difficult for most firms to cultivate this expertise internally.

      Pricing Models

      Pricing structures for managed computer vision services vary, but some common models include:

      Per-Image Pricing

        With per-image pricing, customers pay a fee for each image processed through the computer vision API. For example, a provider may charge $0.001 per image for basic image tagging and classification. The more complex the computer vision task, the higher the per-image rate.

        Per-image pricing provides predictable costs and scales linearly with usage. It’s a good option for sporadic or unpredictable workloads. Paying per image processed avoids high fixed costs.

        Monthly/Annual Subscriptions

          Many providers offer monthly or annual subscriptions for computer vision services, similar to software-as-a-service. For a flat monthly fee, customers get a bundled number of API calls or credits to spend on computer vision tasks.

          Subscriptions allow budgeting for predictable workloads. Subscriptions with generous caps on included API calls can lower the marginal cost per image processed.

          Custom Enterprise Pricing

            Large organizations with high volumes or specialized computer vision needs can negotiate custom enterprise pricing contracts. These provide negotiated rates, volume discounts, and service level agreements.

            Custom pricing creates a partnership between client and provider. The provider scales infrastructure to meet the client’s computer vision needs. In return, the client commits to a certain volume of usage.

            Conclusion

            Managed computer vision services offer significant benefits that make them worth considering for many organizations today. Key advantages include:

              Access to advanced CV capabilities without in-house expertise: Organizations can leverage sophisticated computer vision algorithms without needing to build an in-house team with specialized skills. The service provider handles model development, refinement, and maintenance.

              Faster time to value: With managed services, companies can deploy computer vision capabilities in weeks or months rather than years. The service provider handles infrastructure, model training, deployment, and integration.

              Cost efficiency: Pay-as-you-go pricing means companies only pay for what they use instead of overprovisioning on-prem resources. Managed services also remove the need for significant upfront investments in infrastructure and skills.

              Scalability: Managed CV services easily scale usage up and down on demand, allowing companies to start small and expand as needed. Providers handle capacity planning and provisioning.

              Regular upgrades: Providers continuously update models and infrastructure, so users benefit from accuracy improvements, new capabilities, and technology advances without any effort.

              Flexibility: Users can customize some parameters and input data formatting while relying on the provider for the core CV technology. This balance enables flexibility without taking on full model ownership.

              Organizations that have promising use cases but lack in-house CV expertise and resources should strongly consider leveraging managed services. The most suitable users are those looking to enable new data-driven insights and automation through CV without major investments and skills acquisition. Companies across many industries can benefit, especially when partnering with a provider offering expertise in their vertical. With the right use case, managed computer vision delivers cutting-edge capabilities with reduced costs and complexity.

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