(3.2) Azure ML Studio
This is part of a series of articles called Azure Challenges. You can refer to the Intro Page to understand more about how the challenges work.
During training, Azure Machine Learning creates several pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Using Azure Machine Learning, you can design and run your automated ML training experiments with these steps:
1. Identify the ML problem to be solved: classification, forecasting, or regression
2. Choose whether you want to use the Python SDK or the studio web experience:
2a. For limited or no code experience, try the Azure Machine Learning studio web experience
2b. For Python developers, check out the Azure Machine Learning Python SDK
3. Specify the source and format of the labeled training data: Numpy arrays or Pandas data frame
4. Configure the compute target for model training, such as your local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks.
5. Configure the automated machine learning parameters that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.
6. Submit the training run
7. Review the results
Next step (3.3) Create your compute
