Yes, embeddings can be shared across systems, provided there is alignment in how they are generated, stored, and interpreted. Embeddings are numerical representations of data (like text, images, or other inputs) created by models such as neural networks. To share them effectively, systems must agree on the embedding format, dimensionality, and the context in which they were trained. For example, if two systems use the same embedding model (like BERT for text or ResNet for images) and the same preprocessing steps, the embeddings can be directly transferred. This interoperability is common in machine learning workflows, where embeddings generated in one pipeline (e.g., a Python-based training system) are reused in another (e.g., a Java-based inference service).
A practical example is sharing word embeddings between natural language processing (NLP) systems. Suppose a team trains a custom Word2Vec model on domain-specific text data and saves the embeddings as vectors in a file (e.g., a CSV or binary format). These embeddings could then be loaded into a separate recommendation system or search engine that uses the same vector dimensions and tokenization rules. Another example is using pre-trained image embeddings from a model like CLIP in a downstream application, such as a content moderation tool. The embeddings can be serialized (e.g., using NumPy arrays or Protocol Buffers) and shared via APIs, databases, or distributed file systems, reducing redundant computation and ensuring consistency.
However, challenges arise when systems have mismatched requirements. For instance, if one system uses 512-dimensional embeddings from a fine-tuned model while another expects 768-dimensional vectors from a base model, direct sharing won’t work without conversion. Differences in normalization (e.g., L2-normalized vs. raw embeddings) or tokenization (e.g., word-based vs. subword tokenizers) can also break compatibility. To mitigate this, teams often standardize embedding formats, document metadata (like model versions or preprocessing steps), or use intermediate layers to adapt embeddings. Tools like ONNX or TensorFlow SavedModel can help export embeddings in portable formats, ensuring cross-system compatibility when technical constraints are addressed upfront.