  State-of-the-art NLP inference uses enormous neural architectures and models
trained for GPU-months, well beyond the reach of most consumers of NLP. This
has led to one-size-fits-all public API-based NLP service models by major AI
companies, serving large numbers of clients. Neither (hardware deficient)
clients nor (heavily subscribed) servers can afford traditional fine tuning.
Many clients own little or no labeled data. We initiate a study of adaptation
of centralized NLP services to clients, and present one practical and
lightweight approach. Each client uses an unsupervised, corpus-based sketch to
register to the service. The server uses an auxiliary network to map the sketch
to an abstract vector representation, which then informs the main labeling
network. When a new client registers with its sketch, it gets immediate
accuracy benefits. We demonstrate the success of the proposed architecture
using sentiment labeling, NER, and predictive language modeling
