The AI technology stack encompasses the infrastructure layer, model development layer, application development layer, and the application layer, covering data cleaning, model training and fine-tuning, multi-model parallel RAG engineering, and the convenience and flexibility of application development with HaxiTag Studio.
Infrastructure Layer: The Foundation of AI Operations
The infrastructure and computing layer of AI technology provides essential tools and resources for model serving and computational resource management. Tools like VLLM and NVIDIA’s Triton facilitate model serving, while Skypilot manages computational resources. Advanced vector search and database technologies such as Faiss, Milvus, Qdrant, and LanceDB offer robust data support. This layer is crucial in providing the hardware and software backbone for AI operations.
Model Development Layer: Crafting and Optimizing Intelligent Models
The model development layer is pivotal in AI model design, training, task fine-tuning,and align,.etc. Leveraging frameworks like Transformers and PyTorch, researchers and developers can build and train complex AI models. Additionally, utilizing HaxiTag Studio for data cleaning, training, and fine-tuning further enhances model quality and efficiency.
The Model Development Layer involves key processes such as pre-training, fine-tuning, architecture design, data annotation, and hyperparameter tuning in AI model development. These activities are essential for creating and optimizing AI models for specific tasks and applications.
Development and tool chain Layer: The Innovation Engine of AI Engineering
The application development layer enables multi-model parallel RAG engineering using HaxiTag Studio, providing convenience and flexibility to developers. This layer fosters the development and deployment of various AI applications, spanning coding assistance, workflow optimization, and information management across diverse domains. It serves as a critical bridge in translating AI technology into practical solutions.
Application Layer: Real-world Deployment of AI Technology
The application layer represents the pinnacle of the AI technology stack, encompassing a wide range of practical applications powered by AI models. Leveraging the convenience and flexibility of HaxiTag Studio, developers can quickly build multi-model applications to address real-world challenges such as coding assistance, workflow optimization, and information management.
Model Repositories: Platforms for Sharing AI Innovations
Beyond the core layers, model repositories like CompVis/stable-diffusion, OpenAI/Whisper, and FacebookResearch/LLAMA play a vital role in the AI ecosystem. These repositories facilitate the sharing of model code, fostering collaboration and driving continuous innovation in AI application domains.
The cohesive collaboration among the layers and tools within the AI technology stack propels the widespread adoption and continuous advancement of artificial intelligence technology. From infrastructure to model development, application engineering, and real-world deployment, each layer plays a critical role in driving unprecedented innovation and efficiency across industries. As AI technology evolves, its impact across various domains is expected to deepen and expand further.