This project is part of the Udacity Azure ML Nanodegree. In this project, we build and optimize an Azure ML pipeline using the Python SDK and a provided Scikit-learn model. This model is then compared to an Azure AutoML run.
- ScriptRunConfig Class
- Configure and submit training runs
- HyperDriveConfig Class
- How to tune hyperparamters
In 1-2 sentences, explain the problem statement: e.g "This dataset contains data about... we seek to predict..."
In 1-2 sentences, explain the solution: e.g. "The best performing model was a ..."
Explain the pipeline architecture, including data, hyperparameter tuning, and classification algorithm.
What are the benefits of the parameter sampler you chose?
What are the benefits of the early stopping policy you chose?
In 1-2 sentences, describe the model and hyperparameters generated by AutoML.
Compare the two models and their performance. What are the differences in accuracy? In architecture? If there was a difference, why do you think there was one?
What are some areas of improvement for future experiments? Why might these improvements help the model?
If you did not delete your compute cluster in the code, please complete this section. Otherwise, delete this section. Image of cluster marked for deletion