Behind the 'Deification' of Vertical Industry Large Models: AI Data Services Enter the 'Deep Water Zone'
-
The wave of large models sparked by ChatGPT has swept from general domains to vertical fields. Currently, more industries are developing specialized large model products for vertical segments to accelerate the scenario-based implementation of AI applications.
For example, in the e-commerce sector, platforms and merchants are leveraging large models to reshape various retail processes. Intelligent shopping assistants, for instance, can provide consumers with product recommendations, shopping guides, and itinerary suggestions based on vast amounts of consumer data. Simultaneously, large models can interpret detailed data for countless products, quickly generating key visuals, marketing posters, and product detail pages required for e-commerce operations.
Beyond this, vertical e-commerce large models can also utilize deep learning on massive datasets to master specific pre- and post-sales communication techniques, supply chain order processing, and more, revolutionizing retail models and consumer experiences in the e-commerce field.
As applications yield results, the value of vertical industry large models is being unleashed, continuously enhancing digital productivity. At this juncture, data—one of the three pillars of artificial intelligence—has been elevated to a new strategic level. It’s evident that the value of vertical industry large models relies heavily on the support of massive datasets.
Correspondingly, upstream AI data service providers have proposed new solutions for data challenges. During the 2023 China International Fair for Trade in Services, leading domestic AI data service provider Testin Data upgraded its 'AI Engineering Data Solution' released last year, introducing a full lifecycle AI data solution tailored for vertical industry large models to provide critical support for their implementation.
With large model applications as the pivot, driven by extensive market demands, the entire AI industry chain is undergoing transformative changes, with data-level upgrades becoming increasingly prominent.
Behind the 'Deification' of Large Model Applications: What Kind of AI Data Services Are Supporting Them?
The rise of large models has accelerated the application of AI while introducing new challenges to the entire algorithm industry chain. In terms of data, traditional processes like data production, collection, processing, and storage no longer meet current market demands or efficiently address emerging issues.
As downstream AI applications integrate large model technology for upgrades, upstream AI data services also face transformation. So, what kind of AI data services are needed and suitable for vertical industry large model scenarios?
1. Standardization
The emergence of vertical industry large model technologies corresponds to a significant increase in AI data demand. To meet this surge in scenario-specific data needs, the priority is not blindly expanding data production but improving the universality and usability of AI data—standardization. Avoiding 'futile efforts' in AI data services and ensuring data authenticity, effectiveness, and usability are key to addressing the explosive growth demands of vertical industry large models.
For instance, in the intelligent connected vehicle industry, the release of group standards such as Requirements and Methods for Labeling LiDAR Point Cloud Data of Intelligent Connected Vehicles (T/CSAE 213-2021) and Requirements and Methods for Image Annotation of Intelligent Connected Vehicle Scenario Data (T/CSAE 212-2021) has provided practical methods for labeling scenario data point clouds, significantly advancing the R&D and testing of intelligent connected vehicles.
The most intuitive observation is that in the past, companies in the AI data service industry had varying requirements and methods for image annotation, resulting in inconsistent annotation output files that severely hindered the unified use of data. With the release of relevant standards, the annotation process and the format of annotation results have been standardized, thereby improving the universality of annotated data.
In this process, industry-leading manufacturers often become the pioneers of standards, enabling them to gain greater influence and initiative in subsequent market regulations. For example, Testin Cloud Testing participated in the development of the two major standards for intelligent connected vehicle scenario data.
This leading AI data service provider, while contributing its expertise and technical capabilities to industry standards, has also more quickly and effectively mastered the standardization of data annotation scenarios and applied it to its own products and solutions. Based on its understanding of industry standardization, Testin Cloud Testing's AI data solution for vertical industry large models not only provides large-scale perception data capabilities but also helps autonomous driving companies reduce data collection cycles, improve annotation efficiency, and cut costs—empowering these companies to lead in data-driven R&D.
2. Engineering
With the integration of large model technology, the application of AI is accelerating. On the supply side, AI data services are also undergoing upgrades across the entire lifecycle—data production, collection, processing, refinement, and storage—to meet the rapidly growing demand for AI data. Simply put, in line with the broader trend of AI engineering, AI data services are experiencing a deep engineering upgrade.
Here, Testin Cloud Testing's "AI Data Solution for Vertical Industry Large Models" presents a relatively clear path. Through rich annotation tools, mature API integration, efficient data loops, personnel and project management systems, and secure delivery hardware/software support, Testin Cloud Testing achieves full lifecycle management of the massive data required for vertical industry large models while ensuring data privacy and security.
More intuitively, for every step of AI data services, Testin Cloud Testing provides corresponding tools, technical capabilities, and management systems—much like a mature manufacturing production line—methodically completing tasks from raw data creation to refined processing, all to support downstream vertical industry large models in pre-training.
Take basic data annotation as an example: Testin Cloud Testing currently offers a comprehensive suite of platform tools, including point cloud fusion tracking, OCR transcription, video annotation, speech transcription, speech segmentation, text judgment, text generation, and more, fully meeting the multimodal data annotation needs of vertical industry large models.
3. Scenario-based
This year, the market's focus has shifted from general-purpose large models to vertical industry large models, reflecting the pursuit of practical applications and signaling a trend that could reshape the entire AI industry chain—scenario-based solutions. In the field of AI data services, providers are no longer blindly chasing vast amounts of generic data but are instead targeting effective data for specific domains or scenarios.
Focusing on scenarios is key to accelerating the deployment of vertical industry large models, but it also demands higher standards for AI data services. For example, in intelligent connected vehicles, Testin Cloud Testing's AI data solution includes three components: a foundational database, customized data collection and annotation services, and a full-spectrum data toolchain covering collection, annotation, and management.
In short, as scenario-based trends develop, AI data services will become increasingly customized—not only tailored to specific industries or scenarios but potentially even to individual companies or technical modules.
At the same time, scenario-based AI data demands far exceed conventional requirements. With the continuous upgrading of industry large models, scenario segmentation is becoming increasingly refined, leading to stricter data requirements. In discussions with Intelligent Relativity, Jia Yuhang, General Manager of Testin Data, mentioned that in the field of intelligent connected vehicles, to meet the richness of related scenarios, Testin Data provides AI data services including data collection, data production, and platform tools to fulfill the pre-training needs of large models.
Overall, in line with the upstream and downstream relationships of the AI industry chain, AI data services must cater to the needs of vertical industry large models. Currently, as vertical industry large models accelerate their implementation, there is a growing demand for more, more effective, and more precise scenario data, making the evolution of AI data services evident.
AI Data Services Enter the 'Deep Water Zone': How Can Leading Companies Maintain Their Leadership?
Changes in market trends are often first perceived and reflected by top industry players. In the field of AI data services, Testin Data has proposed an 'AI Data Solution for Vertical Industry Large Models,' positioning itself to continue leading the AI data service industry as vertical industry large models further explode in the latter half of the year.
But is simply entering the market early enough to achieve industry leadership? Clearly not.
Aligning with the market demands of vertical industry large model development, the more critical value of Testin Data's 'AI Data Solution for Vertical Industry Large Models' lies in the three key mindsets behind it.
1. Focus on the Track, Emphasize Value Return
The explosive growth of vertical industry large models has intensified the 'Hundred Models Battle,' with various industries developing their own large model products. While this presents many market opportunities, the corresponding demand for AI data services is also evolving. This means that a generic approach cannot be used for AI data services tailored to vertical industry large models, and it is challenging to cover all industries comprehensively.
In this process, companies must make strategic choices. Currently, Testin Data's 'AI Data Solution for Vertical Industry Large Models' is primarily implemented in sectors such as retail e-commerce, financial insurance, and intelligent connected vehicles. These are industries Testin Data has focused on since its inception, with accumulated data, industry knowledge, project experience, and client resources.
According to Jia Yuhang, General Manager of Testin Data, the key priority in providing AI data services for vertical industry large models is value consideration.
On one hand, the focus should be on areas with existing accumulation and foundation. Following this principle, while offering scenario-based data collection solutions, Testin Data also provides specialized evaluation systems and services tailored to industry needs after fine-tuning tasks, enhancing the value of AI data services.
On the other hand, despite the popularity of vertical industry large models, their commercialization remains limited. For Testin Data, leveraging past service and project experience to identify and deepen focus on high-demand sectors is a responsible approach to corporate development, avoiding the pitfalls of an overheated market and ensuring sustained value.
2. Vertical and Horizontal Upgrades, Strengthening Core Competencies
The application and implementation of vertical industry large models involve a process of coordinating horizontal and vertical capabilities. Jia Yuhang, General Manager of Testin Data, likens this process to 'building blocks'—strengthening the foundational capabilities horizontally to ensure stability, while delving deeply into vertical scenarios, fine-tuning and optimizing solutions for different industries to provide professional outcomes.
Specifically, Testin Data has upgraded its AI-engineering-based data services by constructing a comprehensive AI data service solution horizontally. This includes data visualization, expandable tool modules, and a data permission management system to meet the data requirements for large model pre-training.
Vertically, Testin Data offers specialized solutions tailored to different scenarios based on industry insights. For example, it addresses the advanced needs of vertical industry large models in areas such as external environmental perception for smart connected vehicles, in-car intelligent cockpits, and human motion recognition.
3. Upholding Ethical Standards and Avoiding Industry Sensitivities
Data is inherently sensitive, and vertical industry large models, built on massive datasets, impose stringent requirements on data. Data security is one aspect, while the uniqueness, authority, and validity of data are equally important.
As a leading company in the industry, Testin Data prioritizes data privacy and security in its operations. Jia Yuhang emphasizes that to ensure the legality and compliance of data used for training vertical industry large models, Testin Data signs data authorization agreements with corporate clients. Additionally, over the years, Testin Data has developed proprietary datasets to help industry clients access high-quality data. Furthermore, Testin Data is an AI data service provider compliant with ISO27001 and ISO27701 standards, and it has obtained certifications such as ISO9001, ISO20000, and CMMI3.
Conclusion
As a production resource, AI data is a crucial driver of the AI industry's development and commercialization. In other words, the quality of data significantly determines the extent of AI implementation—a viewpoint echoed by Jia Yuhang.
The explosive growth of vertical industry large models signifies a surge in demand for AI data. Only with high-quality data as the foundation can the implementation of these models hold real significance. This presents an opportunity for AI data service providers and is a key factor in the breakthrough development of the AI industry.
*All images in this article are sourced from the internet.