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Let’s ground this with a concrete example. The spaCy library is a well-known use case for NLP tasks. We’ll use it in a notebook and deploy it via Modelbit.
Install the spacy package and download a trained English pipeline. We’ll use the large version, en_core_web_lg:
pip install spacypython -m spacy download en_core_web_lg
The spaCy package makes it easy to segment sentences. First, import spacy and load the pre-trained en_core_web_lg:
import spacy
nlp = spacy.load(“en_core_web_lg”)
Then create a function that uses nlp to process the text and extract sentences:
def segment_sentences(text: str):doc = nlp(text)return [s.text.strip() for s in doc.sents]
# Example callsegment_sentences(“Fish live in the ocean. Birds live in trees.”)
You’re now ready to deploy segment_sentences to Modelbit after logging in:
import modelbit
mb = modelbit.login()
mb.deploy(segment_sentences)
You’ll see that nlp gets pickled and sent up with the rest of your deployment. When calling your model it returns a list of sentences. For example, a REST API call:
curl -s -XPOST “https://…modelbit.com/v1/segment_sentences/latest” -d ‘{“data”: “Fish live in the ocean. Birds live in trees.”}’
Which returns:
{“data”: [“Fish live in the ocean.”, “Birds live in trees.”]}
The dashboard allows you to check the latencies and runtimes of requests, change compute environments, track usage etc.
In a single afternoon, you built a basic fully functional MLOps deployment without touching Kubernetes once. While this example is minimal, the infrastructure setup scales really well.
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