Productionalization is the process of taking the work we've done so far and making it accessible to the end user. This is by far the most important process. If we are unable to connect the end user to the model, then everything up until now was for nothing. Fortunately, this is where Azure Machine Learning really differentiates itself from the rest of the data science tools on the market. First, let's create a simple experiment that takes our testing data and scores that data using our trained model. Remember that we investigated the use of some basic engineered features, but found that they didn't add value.
|Set Up Web Service|
|Deploy Web Service|
|Web Service Deployment|
|Azure Machine Learning Web Services Portal|
|Azure Machine Learning Web Service Consumption Information|
|Sample Web Service Code|
Hopefully, this post sparked your imagination for all the ways that you could utilize Azure Machine Learning in your organization. Azure Machine Learning is one of the best data science tools on the market because it drastically slashes the amount of time it takes to build, evaluate and productionalize your machine learning algorithms. Thanks for reading. We hope you found this informative.
Data Science Consultant