LLMOps

LLMOps (Large Language Model Operations) is a comprehensive set of practices and processes that cover the development, deployment, maintenance, and optimization of large language models (such as the GPT series). LLMOps aims to ensure the efficient, scalable, and secure use of these powerful AI models to build and run real-world applications. It involves model training, deployment, monitoring, updating, security, and compliance.

The table below illustrates the differences in various stages of AI application development before and after using DIN's service layer:

Steps	Before	After	Save time
Developing Frontend & Backend for Applications	Integrating and encapsulating LLM capabilities requires a lot of time to develop front-end applications.	Directly use backend services to develop based on a WebApp scaffold.	-80%
Prompt Engineering	Can only be done by calling APIs or Playground.	Debug based on the user's input data.	-25%
Data Preparation and Embedding	Writing code to implement long text data processing and embedding.	Upload text or bind data sources to the platform.	-80%
Application Logging and Analysis	Writing code to record logs and accessing databases to view them.	The platform provides real-time logging and analysis.	-70%
Data Analysis and Fine-Tuning	Technical personnel manage data and create fine-tuning queues.	Non-technical personnel can collaborate and adjust the model visually.	-60%
AI Plugin Development and Integration	Writing code to create and integrate AI plugins.	The platform provides visual tools for creating and integrating plugins.	-50%

Before using an LLMOps platform, developing applications based on LLMs can be cumbersome and time-consuming. Developers need to handle tasks at each stage independently, leading to inefficiencies, scaling difficulties, and security issues. Here is the development process before using an LLMOps platform:

1. Data Preparation: Manually collect and preprocess data, which may involve complex data cleaning and annotation work, requiring a significant amount of code.

2. Prompt Engineering: Developers can only write and debug Prompts through API calls or Playgrounds, lacking real-time feedback and visual debugging.

3. Embedding and Context Management: Manually handling the embedding and storage of long contexts, which can be challenging to optimize and scale, requiring a fair amount of programming work and familiarity with model embedding and vector databases.

4. Application Monitoring and Maintenance: Manually collect and analyze performance data, possibly unable to detect and address issues in real-time, and may even lack log records.

5. Model Fine-tuning: Independently manage the fine-tuning data preparation and training process, which can lead to inefficiencies and require more code.

6. System and Operations: Technical personnel involvement or cost required for developing a management backend, increasing development and maintenance costs, and lacking support for collaboration and non-technical users.

With the introduction of an LLMOps platform, developing applications based on LLMs becomes more efficient, scalable, and secure. Here are the advantages of developing LLM applications:

1. Data Preparation: The platform provides data collection and preprocessing tools, simplifying data cleaning and annotation tasks and minimizing or eliminating coding work.

2. Prompt Engineering: WYSIWYG Prompt editing and debugging, allowing real-time optimization and adjustments based on user input data.

3. Embedding and Context Management: This automatically handles the embedding, storage, and management of long contexts, improving efficiency and scalability without the need for extensive coding.

4. Application Monitoring and Maintenance: Real-time monitoring of performance data, quickly identifying and addressing issues, ensuring the stable operation of applications, and providing complete log records.

5. Model Fine-tuning: The platform offers one-click fine-tuning functionality based on previously annotated real-use data, improving model performance and reducing coding work.

6. System and Operations: The user-friendly interface is accessible to non-technical users, supports collaboration among multiple team members, and reduces development and maintenance costs. Compared to traditional development methods, the service layer offers more transparent and easy-to-monitor application management, allowing team members to understand the application's operation better.