The above diagram illustrates a comprehensive tech stack attemp for LLMOps (Large Language Model Operations) tailored for generative AI use cases with Huawei Tools and Open Source Toolkits.
Adapting Pre-Trained LLM to Downstream Tasks
LLM Selection
- Hugging Face Hub: Provides a repository of open-source large language models.
- Ollama with Open WebUI: Includes tools for various AI applications like Chat, Whisper WebUI (Voice), and Stable Diffusion WebUI (Image).
Data Integration & Privacy Measures
- Huawei Cloud DataArts: Manages data integration and ensures privacy measures.
Fine-Tuning
- Huawei Cloud ModelArts: Platform for training and fine-tuning models.
- Pinecone: Vector database for managing and searching embeddings.
- Chroma: Supports embedding management and retrieval.
In-Context Learning & Embeddings
- LangChain: Framework for developing applications powered by large language models.
- Pinecone, Elasticsearch, Hugging Face, Chroma: Tools supporting in-context learning and embedding management.
RLHF (Reinforcement Learning from Human Feedback)
- Stable-Baselines3: An improved implementation of reinforcement learning algorithms. Illustrates the concept of using reinforcement learning to enhance model performance based on human feedback.
End User Management
Prompt Engineering
- LangChain: Used for crafting and managing prompts.
Context Management
- LangChain: Manages context for language models to ensure relevant responses.
Production-Ready Deployment
Model Deployment
- Huawei Cloud ModelArts: Facilitates the deployment of trained models.
- Terraform, Huawei CCE Kubernetes, Docker, GitHub, GitLab: Tools for infrastructure as code, containerization, and continuous integration/deployment.
Model Performance/Monitoring
- Huawei Cloud ModelArts: Monitors model performance.
- Prometheus: An open-source monitoring system.
- Elasticsearch, Logstash, Kibana: Tools for logging, monitoring, and visualizing performance metrics.
Enterprise Model Governance
Model Risk Governance
- Trusted-AI/AIF360: Ensures fairness and transparency in AI models.
- SHAP: Tool for model interpretability.
Model Security
- Open Policy Agent (OPA): Enforces policy-based security.
- Trivy: Vulnerability scanner for containers and other environments.
Model Architecture Optimization
Multiple Models Integration
- LangChain, Dask, Ray Serve: Tools for integrating multiple models and managing their interactions.
Model Interactions Caching
- Huawei Cloud Redis (DCS): Caches interactions to improve performance and reduce latency.
Model Compression
- TensorFlow Model Optimization Toolkit: Provides quantization and pruning for model optimization.
- Apache TVM: Deep learning compiler for model compression.
Routing & Flows of User Prompts
- Apache Camel: Manages routing and flows of user prompts.
Compute Optimization
Runtime/Runtime Accelerator
- ONNX Runtime, TensorRT: Tools for optimizing runtime performance of models.
GPU/NPU/LPU Optimization
- NVIDIA CUDA, Groq, RAPIDS: Libraries and platforms for optimizing performance on GPUs and specialized hardware.
This tech stack encompasses the entire lifecycle of LLM operations, from model selection and data integration to fine-tuning, deployment, monitoring, and governance, providing a holistic approach to managing large language models for generative AI use cases.
The above is for a full end to end enterprise grade LLMOps, however, we should always begin with Minimum Viable Product as below:
Minimum Viable Product (MVP) for LLMOps
Creating a Minimum Viable Product (MVP) for LLMOps involves focusing on the essential components needed to manage the lifecycle of large language models, from data ingestion and model training to deployment and monitoring.
Key Components of LLMOps MVP
- Data Integration & Privacy
- Model Training & Fine-Tuning
- Model Deployment
- Model Performance Monitoring
- Model Governance & Security