Learn how to operate machine learning solutions at cloud scale using the Azure Machine Learning SDK. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion, data preparation, model training, and model deployment in Microsoft Azure.
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- Create an Azure Machine Learning workspace using the SDK
- Run experiments and train models using the SDK
- Optimize and manage models using the SDK
- Deploy and consume models using the SDK
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks, such as Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
- Fundamental knowledge of Microsoft Azure
- Experience writing Python code to work with data using libraries such as Numpy, Pandas, and Matplotlib
- Understanding of data science, including how to prepare data and train machine learning models using common machine learning libraries, such as Scikit-Learn, PyTorch, or Tensorflow
The GitHub repo for this course, containing the code and datasets used, can be found here: https://github.com/cloudacademy/using-the-azure-machine-learning-sdk
We can run experiment scripts using a run configuration and a script run config. Or we can use an estimator. The estimator abstracts both of these configurations in a single object.
So we're going to need the estimator class and experiment class to work with this, and to set up our estimator object, we need to provide the source directory which is training folder, the entry script which is our diabetes training, the PY file, we're doing our computing locally, and conda packages we specify in scikit-learn.
So what we're doing over here is we're using a generic estimator object to run at the training environment. The default environment for this estimator does not include scikit-learn package. So that's what we need to provide that as a parameter to set it up. So you need to expertly add that to the configuration and anything else that is required.
Now at the conda environment is built on demand the first time estimator is used, and cache for federal RAMs that use the same configuration. So the first run will take a little longer. And then when your subsequent runs, the cached environment can be reused, so they will complete more quickly.
After quitting the estimator object, we create an experiment, specifying the usual parameters, the workspace, as well as the name of experiment which is diabetes training. We then run the experiment based on the estimator. So we submit that, and then we wait for completion. So you can see some of the details that are shown as an output after the run.
So you have your run ID, and additional information. So just like with any other experiment run, we can use run details widget to view information about the run and get a link to Azure Machine Learning Studio. We can also retrieve metrics and outputs from the run object.
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