Machine Learning Engineer with Microsoft Azure | 5
Need Help? Speak with an Advisor:
www.udacity.com/advisor
Capstone Project
The program capstone gives you the opportunity to use the knowledge you have obtained from this Nanodegree
program to solve an interesting problem. You will have to use Azure’s Automated ML and HyperDrive to solve a
task. Finally, you will have to deploy the model as a webservice and test the model endpoint.
CapstoneProject
Training and Deploying a
Machine Learning Model
in Azure
You will be using both the hyperdrive and automl API from
azureml to build this project. You can choose the model you want
to train, and the data you want to use. However, the data you use
needs to be external and not available in Azure’s ecosystem. After
you have chosen a dataset, you will have to import the dataset
into your workspace. Subsequently, you will train a model on
that dataset using automated ML and then train a custom model
whose hyperparameters you have tuned using HyperDrive. The
type of model you use is not important. You can use ML models
through Scikit-learn or Deep Learning models like ANNs and CNNs
through Keras, TensorFlow, or PyTorch for this part of the project.
After you have trained both the models, compare their
performance, deploy the best model as a webservice and test the
model endpoint.
This project will demonstrate your ability to use an external
dataset in your workspace, train a model using the different tools
available in the AzureML framework as well as your ability to
deploy the model as a web service.
Dostları ilə paylaş: