How do you deploy embeddings in production?

Deploying embeddings in production involves three key phases: serving the embedding model, integrating with downstream systems, and optimizing for performance. First, you need a reliable way to generate embeddings at scale. This typically involves serving a pre-trained machine learning model (e.g., BERT, Word2Vec) via an API or embedding it directly into your application. For example, you might use TensorFlow Serving or ONNX Runtime to deploy the model as a REST endpoint, allowing other services to send text, images, or other data and receive vector representations in return. To handle high traffic, you’d scale horizontally using Kubernetes or serverless functions, and leverage hardware acceleration (GPUs/TPUs) for computationally intensive models.

Next, infrastructure design is critical. Embeddings are often used for tasks like search, recommendations, or clustering, which require efficient storage and retrieval. Vector databases like FAISS, Pinecone, or Milvus optimize for fast similarity searches by indexing high-dimensional vectors. For instance, a recommendation system might precompute embeddings for millions of products and use FAISS to find the top matches for a user’s query in milliseconds. Caching is another optimization—storing frequently accessed embeddings (e.g., popular search terms) in Redis or Memcached reduces redundant computation. Monitoring latency, error rates, and cache hit ratios ensures the system remains responsive as usage grows.

Finally, versioning and updates must be handled carefully. When retraining or updating an embedding model, backward compatibility is essential to avoid breaking downstream systems. A common approach is to run new and old models in parallel during a transition period. For example, a semantic search application might use versioned API endpoints (e.g., /embed/v1, /embed/v2) and gradually shift traffic to the new model. Data pipelines should also validate input formats and embedding dimensions to prevent mismatches. Additionally, automated testing ensures updates don’t degrade performance—like verifying that a new model maintains >95% accuracy on a test dataset of known query-result pairs.