How does the operators do not do a back pot? AIOPS knows!

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Huawei Naie AI Model and Training Services Minister Yang Jian

Huawei Aiops service three major capacity value can be summarized as:

One, support universal data sources. Support the mainstream data docking mode of Kafka, database, file system, RESTful and other operation and maintenance systems, let data. With data management SDK mode, heterogeneous data (timing data, non-structured data, text data, etc.) is used to enter data into AIOPS atomic capability standard, which effectively solves the data adaptation problem.

Second, there is a wealth of AIOPS atomic capabilities. The AI ??model and algorithm in the operation and maintenance field have the high development threshold, the development cycle is characterized, and the development of public capacity is developed, resulting in waste of resources. Huawei provides digital basic components, improves AI application development efficiency, and reduces AI development. Through industry knowledge and AI algorithm fusion, optimize and self-research AI algorithm, built-in telecommunications field business model parameters, support design gentletening and operational dimensions. In addition, Huawei AIOPS service atomic ability model is unified, so that atomic ability can be connected.

Third, the scene combination is arranged with DevOps. By arranging capacity, there is no need to write code, only the visual drag and drop-type processes are required to reduce the threshold for the rapid development of AI.

Operation scenario AIOPS can be customizable, wherein the process is connected in series, the service generalization parameter configuration, event notification, visualization Dashboard processes, etc., fast custom operation. AIOPS atomic ability scalable, AI platform support algorithm model innovation and development can expand AIOPS capabilities.

The three core value capability solves the data docking, data adaptation, and customization, expansion and other issues in the AIOPS construction process, and the rich telecommunications field AIOPS atomic capabilities are also integrated with industry depth, subject to the disease, let Telecom customers Useful to worry with confidence.

Cooperation Win-Win, Building a network AI ecology

It is reported that Huawei AiOPS service has achieved scale applications in the industry, covering 4 business areas. 110+ extensive network, monthly API call reached 415 million, 250,000 alerts per day.

In order to better serve customers, the AIOPS service uses a service schema. However, just as previously said, the development of AI applications in various fields requires industry experience and AI algorithm experience, and the development is difficult. On the one hand, Huawei AiOps precipitated typical scenes (such as operators network, park network, DC network, IT applications, etc.) AI assets, helping partners quickly pick up, and easy deployment of AI applications. On the other hand, Huawei adheres to the consistent open attitude, supporting the partner development industry app, supporting partners to release AI to apply to the AI ??market, work together, and build network AI ecology.

No manufacturer can meet all business needs, enterprise-level market ecology is the key, and the AIOPS field is also the same, Huawei, more than 30 years in the enterprise market, can win a win-win situation.

Digital transformation is accelerating, increasingly dispersed, heterogeneous and highly dynamic modern IT environment with cloud computing, container and micro-service technology to all corporate organizations including telecom operators. More operation and maintenance challenges. AIOPS has become the first choice for intelligent operation and maintenance, MARKETSANDMARKETS Research found that the global IT owner is purchasing AIOPS tools.

AIOPS service is a crystalline that Huawei 30 years of industry accumulation and new technologies such as the frontier AI. I believe that Huawei AIOPS service will also be possible to help telecom network operation and maintenance intelligence transformation.

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