Research Director, Computer Vision and AI
The human vision system, which gathers and interprets information through sight, remains a critical aspect as part of one’s life as both a consumer and an employee (i.e., working as part of a private business or government entity). Vision is critical to performing routine tasks like navigating roadways and sidewalks; identifying, classifying, and interacting with objects and environments; and engaging with computers and digital devices. Human vision continues to develop and be fine-tuned by technology to support an ever-increasing range of dynamic events and human experiences. Yet, as our society continues to invest in R&D to advance and deploy new technology and automation techniques, there are increasing opportunities for businesses and consumers to leverage or pair (i.e., in a cooperative or human-in-the-loop manner) human sight with computer-driven sight (referred to as computer vision [CV] or computer vision artificial intelligence [CV AI]) to take the next step in delivering improved productivity, efficiency, safety, sustainability, and inclusivity.
CV has been a strong beneficiary of academic and commercialization investments to advance the fields of deep learning– and machine learning (ML)–based approaches to AI. These advancements, which have largely occurred over the past five years, look to abstract the human intelligence schema and system to interpret unstructured data in the forms of images, videos, and sensor data (e.g., radar, lidar) through complex neural networks. To develop this neural network architecture, CV technology user organizations require massive amounts of use case–specific or even generalizable training data, as well as extensive computational resources (including GPUs, TPUs, and hardware- and software-based accelerators) to train, build, and validate models that can “learn” details and characteristics from new, unstructured visual-based inputs. This approach to solving CV AI has led to breakthroughs where computers are now able to surpass the quality and efficiency of humans for multiple discrete use cases, along with delivering differentiated benefits versus humans in the areas of scale, repeatability, longevity, attentiveness, and subjectivity (to name a few).
Although deep learning–based CV is a very new technology area, IDC has seen tremendous progress in its use by organizations of all sizes and across all verticals. This includes support for (or even potentially enabling new) business and consumer use cases that can deliver insights in the areas of:
This IDC MarketScape focuses on one aspect of the CV ecosystem, CV software platform providers. These essential vendors make up the foundation of growth and potential of CV, and they enable customers to understand, experiment, develop, train, validate, deploy, and manage CV models for a near-infinite list of potential use cases. These providers are critical to helping customers extract the complexity of working with, utilizing, and managing CV deployments, as well as helping them understand how cutting-edge AI research techniques and approaches equate ultimately to business value. In many cases, these providers offer different low-code and no-code user interface/user experience (UI/UX) options to support organizations with a mix of potential user personas ranging from AI/ML technical specialists (e.g., data scientists, ML engineers) to traditional IT personnel (e.g., developers and computer programmers) and even line-of-business users (e.g., payroll and accounting staff).
As part of this IDC MarketScape process, IDC spoke with dozens of end-user organizations that are investing in CV platform providers to help them develop and deploy applications. These organizations, which all varied in terms of CV deployment maturity, were almost universally aligned on the tangible, business benefits provided by these CV solutions, as well as (more importantly) recognized that they should have prioritized and invested in CV earlier. These conversations reinforce the need for organizations (broadly) to think through how CV can be used to improve business, consumer, and partner interactions and capabilities both at a strategic, governance level and at a specific use case level.
IDC offers the following advice to technology buyers considering CV:
After a thorough evaluation of Clarifai’s strategies and capabilities, IDC has positioned the company in the Leaders category in this 2022 IDC MarketScape for worldwide general-purpose computer vision AI software platforms.
Clarifai offers a wide range of CV software capabilities, services, and support capabilities for technology buyers looking to build, customize, and deploy CV across a global footprint. Clarifai’s strategy and vision center on the premise of helping customers realize the simplest, most efficient path to derive AI insights and business value from unstructured data modalities including images, videos, text, and audio. Clarifai accomplishes this through its fully integrated AI platform that aims to bridge the components of the ML life cycle to facilitate customer use, adoption, and scale. Clarifai’s development and product team continue to invest to help prioritize and validate which cutting AI edge and usability features should be incorporated into its customer offerings, alongside more typical essential and advanced CV platform capabilities. Some examples of this are the automation and AutoML capabilities integrated into its Scribe Label product for CV annotation; its Spacetime Search product that can index and configure searches across a customer’s portfolio of unstructured images, videos, and document data; its extensive Model Gallery of pretrained models that can be used as is or customized by customers (aka Clarifai’s Community); and its Flare Edge product that simplifies the optimization, deployment, and management of CV inferencing at the edge.
Clarifai’s platform includes thoughtful consideration for maximizing the efficiency of multiple customer profiles whether it be a no-code interface for line-of-business and nontechnical users, a low-code interface for developers, or a fully customizable interface for an organization’s data scientists and ML engineers. These capabilities and interfaces even hold true for ancillary support features including a drag-and-drop workflow tool (Mesh Workflows) that enables customers to build composite model workflows to address complex use cases and scenarios via a drag-and-drop interface.
Clarifai is one of the providers developing and delivering complete, end-to-end CV solutions and should be considered by any organization looking to experiment, learn, or expand its use. Clarifai’s approach to providing comprehensive support for technical and nontechnical users makes the company a strong potential choice for customers looking to democratize CV use across a diverse profile base:
The criteria used for the selection of IT suppliers that were evaluated included the following:
For the purposes of this analysis, IDC divided potential key measures for success into two primary categories: capabilities and strategies.
Positioning on the y-axis reflects the vendor’s current capabilities and portfolio of services and how well aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the company and product today, here, and now. Under this category, IDC analysts will look at how well a vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.
Positioning on the x-axis, or strategies axis, indicates how well the vendor’s future strategy aligns with what customers will require in three to five years. The strategies category focuses on high-level decisions and underlying assumptions about offerings, customer segments, and business and go-to-market plans for the next three to five years.
The size of the individual vendor markers in the IDC MarketScape represents the market share of each individual vendor within the specific market segment being assessed.
IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC judgment about the market and specific vendors. IDC analysts tailor the range of standard characteristics by which vendors are measured through structured discussions, surveys, and interviews with market leaders, participants, and end users. Market weightings are based on user interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and interviews with the vendors, publicly available information, and end-user experiences to provide an accurate and consistent assessment of each vendor’s characteristics, behavior, and capability.
IDC defines a computer vision (CV) software platform as a set of commercialized software tools and technologies that enable customers to design, train, build, validate, deploy, and manage CV artificial intelligence/machine learning (AI/ML) models. These models, when deployed, can derive data-based insights and inferences from unstructured images, videos, and/or sensor data (e.g., lidar, radar, hyperspectral).
General-purpose software platforms are defined as platforms purposely designed to support the broadest range of potential use cases. Although these platforms may contain specialized functions and integrations for a given domain, vertical, or use case, these general-purpose platforms should include capabilities that can broadly address or be applied to most, if not all, use cases.