How are embeddings shared across AI pipelines?

Embeddings are shared across AI pipelines through standardized formats, storage systems, and APIs that enable reuse. When embeddings are generated—such as from a text encoder or image model—they’re typically saved in numerical formats like NumPy arrays, TensorFlow Tensors, or PyTorch tensors. These representations are then stored in databases (e.g., FAISS, Pinecone) or serialized to disk using file formats like HDF5, JSON, or binary files. This allows downstream pipelines to load the embeddings without recomputing them, saving computational resources and ensuring consistency.

For example, a natural language processing (NLP) pipeline might generate word embeddings using BERT and save them as NumPy arrays. A separate recommendation system pipeline could then load these embeddings to compute similarity scores between user queries and product descriptions. Similarly, in computer vision, embeddings from a ResNet model trained for image classification could be reused in a facial recognition pipeline by storing them in a vector database optimized for fast nearest-neighbor searches. Tools like Hugging Face’s Datasets library or TensorFlow Extended (TFX) also provide built-in mechanisms to cache and share embeddings across workflows.

Key challenges include maintaining compatibility between frameworks (e.g., PyTorch vs. TensorFlow) and handling versioning. For instance, if an embedding model is updated, downstream pipelines must either reprocess data or ensure backward compatibility. Solutions often involve standardizing on intermediate formats like ONNX or using cloud storage (e.g., AWS S3) with versioned paths. APIs like REST endpoints or gRPC services can also expose embeddings dynamically, allowing pipelines to fetch them on demand without managing raw files. This approach is common in microservices architectures, where an embedding service runs independently, and multiple pipelines query it via HTTP requests. By centralizing embedding generation, teams reduce redundancy and ensure all pipelines use the same semantic representations.