From MLOps to LMOps: Tackling Large Model Challenges and Solutions

1  From MLOps to LMOps

Machine learning today relies heavily on deep learning models built on large‑scale compute, which have driven AI forward. The transition from traditional DevOps to MLOps standardizes model development, training, deployment, monitoring, and management, while LMOps extends these practices to large generative models.

2  MLOps Overview, Challenges & Solutions

Key challenges include fragmented data and model versioning, long development cycles, insufficient monitoring, and cross‑team coordination. Solutions involve automating data annotation, experiment management with version control, AutoML/AutoDL, model compression, drift monitoring, and building end‑to‑end pipelines on platforms such as Baidu AI Cloud.

3  LMOps Implementation Challenges & Key Technologies

LMOps faces three main technical fronts: inference performance optimization, prompt construction & automatic optimization, and context‑length extension.

3.1  Inference Performance Optimization

Quantization‑aware training (QAT) reduces precision loss by simulating quantization during training, allowing per‑channel and per‑group weight quantization, as well as k/v‑cache int8 quantization, achieving up to 50% memory reduction and 1.5× speedup.

Model sparsity techniques such as SparseGPT and WandA further compress models, with SparseGPT providing up to 60% performance gain for trillion‑parameter models.

3.2  Prompt Construction & Automatic Optimization

Effective prompts are crucial for large models. Approaches include template libraries, fine‑tuned models that translate natural language into prompts, and iterative feedback loops that refine prompts automatically.

3.3  Context‑Length Extension

Techniques such as vector‑database retrieval, Naive Bayes‑based Context Extension (NBCE), and positional interpolation (e.g., RoPE) enable models to handle inputs beyond the native 2K‑3K token limit.

4  Future Outlook

The rapid emergence of open‑source large models (e.g., LLaMA series) and increasing investment in MLOps/LMOps tools suggest a short‑term boom, followed by consolidation around industry‑specific models. Enterprises will continue to rely on LMOps platforms for cost‑effective, scalable deployment and management of AI capabilities.

Overall, the presentation provides a comprehensive roadmap from MLOps fundamentals to LMOps innovations, highlighting Baidu AI Cloud's AI middle‑platform as a practical implementation of these concepts.