The generative AI development process requires:
- Foundation model interface – Provides access to the generative AI models through an API
- Front-end web/mobile application – The user-facing part of the application that runs on websites or mobile devices
- Data processing labeling – Preparing and annotating data to train models
- Model training – Using labeled data to teach model patterns and improve performance
- High-quality monitoring and security tools – Overseeing models, detecting issues, and protecting data and systems
- Vector database – Storage for vector representations of text and images used by models
- Machine learning platform – Infrastructure for developing, testing, deploying, and managing models
- Machine learning network storage – Data storage is networked for efficient ML tasks
- AI model training resource – Computing power like GPUs for model training
- Text-embeddings for vector representation – Representing text as vectors in numerical form for models
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