Machine learning prediction serving
Project description
ServeIt deploys your trained models to a RESTful API for prediction serving. Current features include:
Model prediction serving
Model info endpoint creation
Logging
Supported libraries
Scikit-Learn
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from serveit.sklearn_server import SklearnServer
# fit a model on the Iris dataset
data = load_iris()
reg = LogisticRegression()
reg.fit(data.data, data.target)
# deploy model to a SkLearnServer
eds = SklearnServer(reg, reg.predict)
# add informational endpoints
eds.create_model_info_endpoint()
eds.create_info_endpoint('features', data.feature_names)
eds.create_info_endpoint('target_labels', data.target_names.tolist())
# start API
eds.serve()
Limited functionality
TensorFlow
Keras
PyTorch
Project details
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