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Developers review the model’s outputs, getting a qualitative sense of how it’s performing. Using large models to generate synthetic data is becoming popular because this method speeds up the deployment process, but it’s still important to have humans check the results for quality assurance. For example, a vector database requires processing data into embeddings, optimizing chunking strategies, and ensuring only relevant information is available. This diverse range of data types adds a complexity layer in terms of data organization, tracking, and lifecycle management.<\/p>\n<\/p>\n
In addition, you might need to optimize prompts, apply fine-turning techniques, or change to another foundation model. Secure your enterprise data in storage and transmission by completing model and app development in your dedicated Virtual Private Cloud (VPC) network and accessing data with PrivateLink, apply customizable content governance to prompts and content, and combine responsible AI principles with tools for human accountability Speed up model development workflows with comprehensive tools designed to support SFT and LoRA, built-in model compression and inference acceleration, multi-dimensional model evaluation in visualized templates, and one-click model deployment Deploy your model as an online service or a web app with PAI-EAS, which supports push-button deployment of large-scale complex models<\/p>\n<\/p>\n