内容简介:Putting ML models in production is a challenge; over 60 percent of modelsAs fitting is not the aim of this tutorial, I will just fit an XGBoost model on the standard(I am ignoring model checking and validation; let’s focus on deployment)
The shortest tutorial I was able to write for deploying ML & AI models efficiently
Jun 18 ·3min read
Putting ML models in production is a challenge; over 60 percent of models never actually make it to production . This need not be the case: putting models into production efficiently can be done using only a few lines of code.
Model fitting
As fitting is not the aim of this tutorial, I will just fit an XGBoost model on the standard scikit-learn breastcancer data :
# Get the data: from sklearn.datasets import load_breast_cancercancer = load_breast_cancer()X = cancer.data y = cancer.target# And fit the model: import xgboost as xgbxgb_model = xgb.XGBClassifier(objective="binary:logistic", random_state=42) xgb_model.fit(X, y)
(I am ignoring model checking and validation; let’s focus on deployment)
Deploying the model
Now we have the fitted model; let’s deploy it using the sclblpy
package :
# Model deployment using the sclblpy package: import sclblpy as sp# Example feature vector and docs (optional): fv = X[0, :] docs = {} docs['name'] = "XGBoost breast cancer model"# The actual deployment: just one line... sp.upload(xgb_model, fv, docs)
Done.
Using the deployed model
Within seconds after running the code above I received the following email:
Clicking the big blue button get’s me to a page where I can directly run inferences:
While this is nice, you probably want to make a nicer application to use the deployed model. Simply copy-paste code you need for your project:
And off you go!
Wrap up
There is much more to say about model deployment (and about doing so efficiently: the procedure above actually transpiles your model to WebAssembly to make it efficient and portable), I won’t.
This is just the shortest tutorial I could think of.
Disclaimer
It’s good to note my own involvement here: I am a professor of Data Science at the Jheronimus Academy of Data Science and one of the cofounders of Scailable . Thus, no doubt, I have a vested interest in Scailable; I have an interest in making it grow such that we can finally bring AI to production and deliver on its promises. The opinions expressed here are my own.
以上所述就是小编给大家介绍的《From model fitting to production in seconds》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
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