Unlocking LMOps: How Enterprises Can Master Large Model Operations

Course Overview

The session covers four main topics: the development path of artificial intelligence, LMOps concepts and key technologies, the capabilities of Baidu Cloud's Qianfan large‑model platform, and real‑world industry practice.

From Machine Learning to the Model Boom

Artificial intelligence today relies on machine learning, especially deep learning based on large neural networks. Deep learning eliminated the need for manual feature engineering and became the mainstream AI technique.

Since 2012‑2016, classic deep models such as CNNs, GANs and ResNet achieved breakthroughs in vision, speech and NLP. From 2017 onward, the Transformer architecture dominated NLP and later became the backbone of generative large models, which now have billions to trillions of parameters and are referred to as large language models (LLMs).

LLMs such as ChatGPT and Wenxin Yiyan demonstrate the power of generative models for dialogue and content creation.

Technical and Application Changes Brought by Large Models

Data: Pre‑training requires TB‑PB scale data, often multimodal, instructional or conversational, which differs from classic deep‑learning datasets.

Training & fine‑tuning: Training trillion‑parameter models demands thousands of GPUs/TPUs, new scheduling, fault‑tolerance and communication techniques.

Evaluation: Traditional metrics based on labeled test sets are insufficient; new benchmarks and automated evaluation methods are needed.

Inference: Prompt engineering enables models to follow instructions and generate content, a capability absent in earlier models.

These changes also reshape how AI applications are built: a single pre‑trained model can be adapted to many tasks with minimal data, reducing development cost and time.

From DevOps to MLOps and LMOps

DevOps provides a methodology for the full software lifecycle, including version control, CI/CD, containerization and automated operations.

MLOps extends DevOps to the machine‑learning lifecycle—data collection, preprocessing, model development, training, evaluation, deployment and monitoring—recognizing that code development is only a small part of the overall effort.

LMOps inherits the MLOps framework but adds adaptations for large models, such as handling massive unlabeled data, efficient fine‑tuning (PEFT, LoRA, P‑tuning), reinforcement learning from human feedback (RLHF), and advanced prompt‑engineering tools.

Key LMOps Technologies

Data processing pipelines: cleaning special characters, removing low‑quality documents, deduplication, privacy‑preserving masking, and tokenization (e.g., SentencePiece).

Training techniques: Supervised Fine‑Tuning (SFT), RLHF, and Parameter‑Efficient Fine‑Tuning (PEFT) methods like LoRA and Prefix‑tuning.

Evaluation: New benchmarks, multi‑dimensional metrics (effectiveness, performance, safety, diversity) and automated evaluation tools.

Inference: Prompt templates with task description, context, examples (few‑shot) and query; automated prompt‑generation tools to ensure safety and quality.

Deployment: Model quantization, distillation, cross‑compilation, edge‑device support, and secure serving with encryption, sandboxing and privacy‑preserving inference.

Baidu Cloud Qianfan Large‑Model Platform

Qianfan hosts Baidu’s Wenxin series models and integrates third‑party open‑source models such as Llama 2 and BloomZ. It provides rich compute and storage resources, secure training environments (trusted execution, data encryption, differential privacy), and end‑to‑end toolchains for data processing, PEFT training, model compression, deployment, and monitoring.

Key platform features include:

Ease of use: one‑click model creation and fine‑tuning.

Comprehensive functionality: data annotation, feedback loops, multiple training modes, PEFT, model compression, and plugin‑based inference services.

Reliability and security: trusted execution environments, data encryption, privacy‑preserving mechanisms.

Performance optimization tools that accelerate training and inference.

Open ecosystem: support for third‑party models and extensible plugin mechanisms.

Industry Practice

A demo shows an investment‑advisor application built on Qianfan, where the model analyzes a client’s portfolio, identifies risks and provides actionable recommendations, illustrating how large‑model AI can boost efficiency and creativity in finance.

For large enterprises, a full‑stack AI solution is required to manage AI capabilities across headquarters and subsidiaries, covering cloud‑edge collaboration, data security, and lifecycle management.

Conclusion

The course delivered a complete view of AI evolution, LMOps concepts and technologies, Qianfan platform capabilities, and practical industry scenarios, equipping participants to navigate the transition from traditional machine learning to large‑model‑driven AI.