In practical application scenarios, we divide the users of the enterprise knowledge engine into three core roles: application, builder, and manager. Different roles have different focuses and goals in the process of learning and practical application of product functions. In order to help you learn more specifically and quickly master the core functions and practical skills of the product, this article sorts out exclusive learning paths for different roles and integrates rich supporting learning resources to help you get started with the enterprise knowledge engine efficiently.

Relying on Volcano Engine's advanced large-scale model and big data technology, Enterprise Knowledge Engine is oriented towards various intelligent agent application scenarios in enterprises. It has the ability to manage and apply knowledge of structured and unstructured data from access, processing, modeling, mining to application, etc. It can play a role in intelligent knowledge answering, sales recommendation, product recommendation marketing and other scenarios, helping enterprises to fully explore the value of various types of knowledge in the era of large-scale models.
For more information, please see Overview of the Enterprise Knowledge Engine.

• If you do not purchase and need a trial, please contact the business staff to apply. The business staff can try it after opening the experience whitelist.
• If your company or you have purchased a private deployment version, you need to ask an administrator to create a user, see the details Authorization Management.

Enterprise Knowledge Engine Applicant Learning Path #

Role definition #

Enterprise knowledge engine users are usually front-line business personnel, customer service/shopping guides, ordinary employees, external customers/partners in the enterprise, and need to use the enterprise knowledge engine intelligent Q & A and knowledge service capabilities. They are based on the enterprise's accumulated internal systems, product materials, customer information, marketing words and commodity information and other knowledge assets, and quickly obtain business-required information through intelligent Q & A, knowledge retrieval, scene-based assistants, etc. They focus on the convenience of knowledge acquisition, the accuracy of Q & A results and scene adaptability, covering multiple scenarios such as employee self-service check-ups, marketing words recommendation, and user self-service consultation. Generally, front-line personnel and external users are the main users.

Goal Vision #

As an enterprise knowledge engine application, the goal that can be learned through the learning path is to quickly obtain credible, understandable, and executable knowledge answers around daily business scenarios with a low threshold of use, and no longer rely on manual transfer, experience verification, or cross-system repeated retrieval. Instead, knowledge such as enterprise internal systems, product information, customer information, product information, and marketing words can be truly transformed into first-line usable productivity.

• Acquire knowledge efficiently
  When facing scenarios such as system inquiries, product inquiries, customer Q & A, marketing activities, etc., you can directly obtain the required answers, original documents, and related materials through natural language questions, reducing the cost of searching back and forth between multiple systems and multiple documents
• The answer is accurate
  The system can combine knowledge classification, marking, Knowledge Graph or structured data to reduce the risk of hallucinations and false answers, and meet the accuracy requirements of enterprise-level production scenarios
• Knowledge services are close to actual scenarios
  The retrieval application can be embedded in specific scenarios such as customer service, shopping guide, office collaboration, dealer support, and employee self-service in the form of APIs, becoming an important entrance to complete consultation, investigation, and auxiliary decision-making

Learning Path #

Enterprise knowledge engine builder learning path #

Role definition #

Enterprise knowledge engine builders are usually IT team members, data analysts, knowledge operation specialists, and business experts in the enterprise, and are the "foundation builders" of enterprise knowledge assets and knowledge service capabilities. They are mainly responsible for multi-source data access, unstructured data processing, knowledge base construction and configuration, knowledge slicing and marking, ontology modeling and Knowledge Graph construction, question and answer tuning, AI application docking, etc. At the same time, they need to sort out the business knowledge system, formulate knowledge storage standards, complete knowledge quality verification, and ensure the integrity, accuracy and availability of knowledge assets. Generally, full-time IT/data teams, knowledge operation teams, and business experts in various fields are the main ones.

Goal Vision #

As an enterprise knowledge engine builder, the goal that can be learned through the learning path is to systematically access, clean, model, manage, and precipitate the knowledge scattered in databases, documents, tables, pictures, audio & video, and business systems around the real business scenarios of the enterprise into a knowledge base for sustainable reuse, supporting the stable implementation of applications such as upper-level intelligent Q & A, marketing assistants, and internal knowledge centers.

• Multi-source knowledge access and precipitation
  Unified access to structured and unstructured data inside and outside the enterprise, including databases, object storage, cloud documents, web pages, public domain data, audio & video and business documents, etc., to reduce knowledge islands, form a unified knowledge source, and build a unified management enterprise knowledge base
• Knowledge processing standardization
  Through slicing, marking, cleaning, and AI large-scale model processing, the original content is processed into knowledge units suitable for retrieval and Q & A, and the quality and use effect of subsequent recalls are improved
• Deep knowledge modeling
  On the basis of knowledge access, around the actual business scenarios, complete knowledge management operations such as knowledge base classification, knowledge marking, object relationship extraction, and Knowledge Graph construction, so that knowledge can be moved from "searchable" to "relatable, inferential, and reusable".
• Knowledge retrieval ability landing
  By building a search application, correlate enterprise knowledge base data, and implement it in the actual business scenarios of the enterprise in the form of APIs, such as customer service, shopping guide, office collaboration, dealer support, employee self-service, etc
• Establish a continuously optimized knowledge building mechanism
  Through product capabilities such as knowledge recall testing, application debugging, knowledge version management, data statistics, etc., continuously discover and collect knowledge optimization points, continuously manage and tune, and improve knowledge quality and actual Q & A performance

Learning Path #

Enterprise knowledge engine manager learning path #

Role definition #

An enterprise knowledge engine manager is usually a member of the IT team or department management in the enterprise, and is the head of the operation and governance of the enterprise knowledge engine. Its main responsibilities include the daily operation and maintenance of the platform, user and role management, fine grain authority allocation, knowledge base life cycle management, knowledge security and compliance control, knowledge operation data monitoring and iterative optimization, etc. At the same time, it is necessary to establish a knowledge operation mechanism, promote the continuous update of knowledge assets and value mining, and ensure that the knowledge engine can serve the whole enterprise business scenarios stably, efficiently and safely for a long time.

Goal Vision #

As the manager of the enterprise knowledge engine, the goal that can be learned through the learning path is to build the enterprise knowledge engine into a long-term and stable knowledge infrastructure, and continue to promote the precipitation of knowledge assets, organizational collaboration and business value release under the premise of ensuring security, authority, compliance and system stability, so that the platform can move from "usable" to "common, easy to use and manageable".

• Establish a secure and controllable knowledge authority system
  Build a clear system of project, role, resource, and content permissions, and achieve fine control at the module level, resource level, and knowledge content level to ensure that different roles can safely use knowledge within their respective permissions
• Ensure the stable operation and business continuity of the platform
  Through product functions such as testing, debugging, data statistics, and system monitoring, we manage the operation and maintenance of key links such as knowledge access, knowledge processing, retrieval and Q & A, and open interfaces, and detect and deal with abnormal problems in a timely manner.

Learning Path #