You have built an ML pipeline in order to make your machine learning process reproducible and automated.
For quality assurance reasons, you show your script to one of your senior data scientist colleagues, who, after reviewing it, she suggests using an Estimator step in your pipeline.
Why did she suggest it?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect because the Estimator, despite how its name suggests, doesn't “estimate” and it is not used in the context of model metrics.
This is a configuration object.
Option B is CORRECT because the Estimator combines the Run Configuration and a Script Run Configuration to a single object, making the configuration task easier and more straightforward.
By including it in an Estimator Step, even complex configurations can easily be included in a reproducible pipeline process.
Option C is incorrect because for passing data between pipeline steps, the PipelineData object must be used.
Option D is incorrect because it is the model explainers (like Tabular, Mimic etc.) that help interpret the behavior of the model.
Estimator is a configuration object used to define the run context of the model.
<pre class="brush:java;">#create estimator object.
from azureml.train.estimator import Estimator.
est = Estimator(source_directory=source_directory,
compute_target=cpu_cluster,
entry_script='dummy_train.py',
conda_packages=['scikit-learn'])
# create estimator step using the estimator object.
from azureml.pipeline.steps import EstimatorStep.
est_step = EstimatorStep(name="Estimator_Train",
estimator=est,
estimator_entry_script_arguments=["--datadir",
input_data.as_mount(), "--output", output],
runconfig_pipeline_params=None,
compute_target=cpu_cluster)</pre>
Reference:
Sure, I'd be happy to provide you with a detailed explanation of why the senior data scientist colleague suggested using an Estimator step in the ML pipeline.
An Estimator is a high-level object in machine learning frameworks, such as Azure ML, that abstracts away the details of the machine learning algorithm and provides a consistent interface to the model training process. Estimators can be used to train a wide range of machine learning models, including classification, regression, and clustering.
There are several benefits of using an Estimator step in an ML pipeline, including:
Simplifying the specification of script execution: One of the primary benefits of using an Estimator step in an ML pipeline is that it simplifies the task of specifying how a script executes. With an Estimator, you can define the inputs and outputs of your script in a consistent and concise manner, reducing the risk of errors or inconsistencies.
Enhancing the reproducibility of the model: By using an Estimator, you can ensure that your model training process is reproducible. The Estimator encapsulates the entire model training process, including hyperparameter tuning and data preprocessing, so that you can easily reproduce the results at any time.
Estimating the performance of the model: Estimators can be used to estimate the performance of the model by using the primary metric. The primary metric is a metric that is used to evaluate the performance of the model during training and is typically a measure of how well the model is able to achieve the desired outcome. By using an Estimator, you can get an estimate of the model's performance before deploying it in production.
Enhancing the explainability of the model: While an Estimator does not directly enhance the explainability of the model, it can indirectly contribute to the explainability of the model. By encapsulating the model training process in an Estimator, you can document the entire process and make it easier to understand how the model was trained and what factors contributed to its performance.
In summary, the senior data scientist colleague suggested using an Estimator step in the ML pipeline because it simplifies the specification of script execution, enhances the reproducibility of the model, estimates the performance of the model, and indirectly contributes to the explainability of the model.