Article | Tech Trends by RingCentral

In the midst of the AI revolution ignited by DeepSeek, enthusiasm for powerful and intelligent AI experiences is palpable. Enterprises, too, are flocking to explore the application of DeepSeek. However, despite its cost-effectiveness and open-source availability, which have lowered the barriers to AI adoption, the path to its successful implementation is fraught with challenges.

As DeepSeek is integrated into the core production systems and various facets of an organization, numerous questions arise: Is one DeepSeek model sufficient? Will more advanced models emerge? How can existing models be replaced or integrated? How do we feed existing data into DeepSeek, or even future models? These questions are particularly pressing for CTOs, especially after enterprises have already embarked on their DeepSeek journey.

From the Lab to the Production Floor: The Common 'Pain' of Enterprise AI Applications

Leading enterprises across industries such as government, finance, electricity, coal mining, and manufacturing have publicly announced the adoption of DeepSeek for intelligent applications. Statistically, over 40 of the current 98 central enterprises have launched DeepSeek applications.

While most applications are still concentrated in general scenarios like customer service, office work, and R&D, some leading enterprises are beginning to expand into core scenarios. The next major challenge lies in how to comprehensively leverage DeepSeek to empower the entire production process of enterprises and truly put large models into practice.

1. Hundred Models, Thousand Scenarios: Multi-model Collaboration is Inevitable

The human world, with its complexity and diversity, presents a myriad of scenarios and problems. A single model cannot meet all needs. For instance, in a manufacturing enterprise, there are intelligent Q&A and knowledge base scenarios in sales and service, as well as factory intrusion detection, accurate early warning of abnormal events, and production process optimization. These require a combination of natural language models, machine vision models, and predictive models. While DeepSeek excels in natural language and scientific computing, it is not omnipotent and cannot cover all areas of AI applications.

As most government and enterprise users adopt DeepSeek, previously deployed models will continue to operate in parallel, especially machine vision and predictive models widely used in the industrial field. Over time, model technology will iterate and update at an increasingly rapid pace, meaning new models or training paradigms will inevitably emerge.

Therefore, the coexistence and coordinated development of multiple models is an inevitable choice for the AI industry to meet diversified needs, and this will not be replaced by the emergence of a specific model.

2. Surging Computing Power: Demand for Computing Power Continues to Grow

The advancement of AI technology, particularly the continuous evolution of deep learning models, has led to an exponential increase in the demand for computing power. From early simple neural networks to today's complex large-scale pre-trained models, every technological breakthrough relies on robust computing power support.

While DeepSeek reduces training costs by 90% through algorithm optimization, it also lowers the barrier to entry for computing power, encouraging more industries to embrace AI. In the long run, both the popularization of AI applications and the iteration and update of models rely on continuous improvements in computing power.

Therefore, the upward trend in computing power demand is unstoppable and serves as a crucial material foundation for driving the development of the AI industry, unaffected by the emergence of individual technologies or products.

3. Application is King: Deep Dive into Industry Scenarios with a Systematic Approach

Each industry possesses a unique knowledge system and operational logic. AI can only truly unlock its potential by continuously delving into these industries and optimizing itself accordingly. Thus, in implementing AI applications for government and enterprises, it is essential to deeply integrate general models with industry knowledge to maximize the value of AI technology.

For example, in the financial sector, while leading AI technology is crucial, it is also necessary to analyze multi-source data such as customer transaction data, credit records, and social networks to build risk assessment models. These models enable accurate judgments of customers' credit risks and decisions on loan approvals, loan amounts, interest rates, etc. These are tasks beyond the capability of current general models.

This process requires a scientific methodology and is a systematic project. DeepSeek provides enterprises with a new large model option, but the key steps and principles in the implementation process remain unchanged. These include precise demand analysis, efficient data preparation, model selection and customization, deployment, operation and maintenance, and integration with the enterprise's existing business processes and systems.

First, enterprises need to clarify their business objectives and identify which business processes can be optimized or innovated with large models. Second, when deploying models, it is crucial to consider the enterprise's IT infrastructure, data security, and privacy protection requirements, choosing the appropriate deployment method such as private cloud, hybrid cloud, or edge deployment. High-quality industry data is then required to retrain and fine-tune large models to match the enterprise's actual scenarios. After deployment, model performance must be continuously monitored, adjusted, and optimized based on business changes and user feedback. This methodology ensures that large models deliver value and achieve sustainable development within enterprises, unaffected by the emergence of new models.

In summary, the difficulties and fundamentals of government and enterprise AI applications remain unchanged, determining the direction of AI application development. By firmly grasping the basics and actively planning based on their own development needs and industry characteristics, enterprises can achieve steady and sustainable development.

Bridging the Last Mile: A Sustainable Evolution Platform is Key

The focus of large models is constantly evolving. From ChatGPT to DeepSeek, it took only two years for the market leader to change. In the evolution of technology, change and uncertainty are inevitable. For large government and enterprise users, building an AI architecture with sustainable evolution capabilities as a foundation for coping with future changes is crucial for achieving steady and sustainable development in the AI field.

1. A Stable AI Development Platform

The AI platform serves as a bridge between underlying hardware and software infrastructure and upper-level large models. Its technical architecture must be convergent, simple, and unified. The AI platform needs to encapsulate the complexity of the underlying hardware and software in conjunction with the cloud platform, solve computing power efficiency through elastic resource scheduling, and manage the scheduling and operation of computing power, models, and various resources. Upwards, it must support the rapid adaptation of diverse models and provide a series of toolchains to support one-stop development and deployment of models, data, and applications, streamlining the end-to-end process of fine-tuning, evaluation, compression, and deployment to accelerate model launch.

Furthermore, the AI platform must flexibly select multiple deployment modes such as public cloud, private cloud, or a combination of both, based on the enterprise's needs at different times and in various scenarios. For scenarios with edge business needs, it is necessary to plan the cloud-edge collaboration architecture in advance.

A hybrid cloud solution is particularly suitable. Huawei Cloud's hybrid cloud solution, Huawei Cloud Stack, for instance, offers various deployment modes such as Ascend Cloud Service, full-stack hybrid cloud, and edge lightweight, allowing government and enterprise customers to flexibly choose based on their different AI needs at different stages. In the initial exploration stage, customers can quickly launch application pilots with one-click access to Huawei Cloud Ascend Cloud Service. As they progress to deeper application stages, customers can optionally push models trained on Ascend Cloud to the local central cloud. Based on the full-stack cloud services provided by Huawei Cloud Stack, combined with customers' local private data, the models can be fine-tuned and retrained to develop specialized models that better match their business needs.

In this way, government and enterprise customers can smoothly complete AI application deployment and experimentation at different stages without needing to migrate platforms or reconstruct architectures, efficiently realizing the digital and intelligent transformation and upgrading of enterprises.

2. Standardized Implementation Paradigms

The evolvability of the architecture also stems from its understanding and implementation of AI implementation paradigms. A platform with standardized implementation paradigms can solve various problems in the implementation of government and enterprise AI applications in an orderly manner, usually requiring a series of development tools for support.

For data development, a robust set of data development tools with efficient data collection, cleaning, and preprocessing functions can significantly improve data quality. Based on these high-quality data, DeepSeek models can be retrained and fine-tuned to adapt to enterprise scenarios.

In the model development phase, a series of advanced tools support model design, training, and optimization. These include model retraining, fine-tuning, edge deployment, as well as quantization, compression, and model evaluation, helping developers understand the strengths and weaknesses of the model and make targeted optimizations accordingly.

In the application development phase, efficient and user-friendly application development frameworks enable users to quickly integrate trained models into various application systems, including Prompt templates, prefabricated plugins, RAG, etc., allowing AI application creation in minutes. Meanwhile, visual interface design features lower the development threshold, improve development efficiency, and enable non-professional developers to quickly build fully functional AI applications.

3. Accumulation of Talent and Experience

In addition to technical capabilities, the evolvability of the architecture also relies on the accumulation of talent and experience. As the application process of government and enterprise AI accelerates, these capabilities transform into irreplaceable assets for the platform.

By establishing a sound experience accumulation mechanism, these experiences can be sorted out, summarized, and shared. For example, by creating an internal knowledge base and regularly organizing experience sharing sessions and technical exchange activities, the enterprise can structurally manage its internal experience and knowledge, enabling it to quickly draw on past successful experiences and avoid detours when facing new large model projects.

Simultaneously, a comprehensive AI talent development plan should be formulated to attract and cultivate a group of high-quality AI professionals. Clarify the recruitment criteria, training plans, and growth paths for AI talents. Enterprises should also foster a favorable innovation atmosphere, encourage employees to experiment with new technologies and methods, provide platforms for practice and innovation, stimulate creativity and potential, and build a highly competitive AI talent team.

Conclusion

The AI boom sparked by DeepSeek is redefining the opportunities and challenges facing government and enterprise organizations in the AI field. Faced with the growing demand for computing power and complex, ever-changing business scenarios, enterprises must not only keep pace with the times and actively embrace new models and technologies but also take a long-term perspective, starting from their actual situations and development needs, carefully selecting the right AI architecture and evolution path.

This is not just a process of technology selection but also a test of an enterprise's strategic vision and execution capabilities. Only by choosing a stable, reliable, and flexibly evolving AI platform can government and enterprise AI succeed in key areas such as large-scale deployment, application development, data engineering, and model retraining and fine-tuning, responding to the continuously changing future with a long-term and stable approach.

*All images in this article are sourced from the internet