Discover more examples at Microsoft Machine Learning Server
In these examples, we will demonstrate how to develop and deploy end-to-end advanced analytics Python solutions with ]ML Server](https://docs.microsoft.com/en-us/machine-learning-server/what-is-machine-learning-server). The samples provided here are created in Python. Samples in R are available in the ML Server R Templates repository.
Although these samples have not been written for SQL Server ML Services, they can be deployed in the same manner as the R Templates that are provided in the repository linked above.
Category | Sample | Code | Documentation |
---|---|---|---|
Basic | Use a regression to predict wine quality with RevoscalePy | Code | |
Basic | Use a regression to predict wine quality with microsoftml | Code | |
Basic | Doing feature selection using mutual information | Code | |
Basic | Implementing binary classification | Code | |
Basic | Binary classification | Notebook | Documentation |
Basic | Implementing multi-class classification | Code | |
Basic | Working with categorical features | Code | |
Basic | Using formulas | Code | |
Basic | Using a loss function | Code | |
Basic | Working with input schemas | Code | |
Basic | Classifying images using image featurization | Code | |
Basic | Finding similar images using image featurization | Code | |
Advanced | Tuning model hyperparameters using a grid search | Code | |
Advanced | Implementing sentiment analysis | Code | |
Advanced | Implementing text featurization | Code | |
Operationalization | Deploy Python model as a web service | Notebook | Documentation |
Operationalization | Integrate a real-time web service into an application | Notebook | Documentation |
Operationalization | Consuming web services synchronously | Notebook | Documentation |
Operationalization | Consuming web services asynchronously | Notebook | Documentation |
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