Azure Machine Learning Service Hyperparameter Tuning

In the code below we kick off a hyperparameter tuning run using the Python SDK. It could be trained on your local machine remote servers Azure Machine Learning kubernetes-based services like Kube Flow Adapt DL Open pal etc.

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Overview of Hyperparameter Tuning In Azure.

Azure machine learning service hyperparameter tuning. It compares metrics over all models to get the combinations of settings. The goal is to determine the optimum hyperparameters for a machine learning model. Accelerate model creation with automated machine learning and access powerful feature engineering algorithm selection and hyperparameter-sweeping capabilities.

1 day agoThis week The New Stack kicks off a deep dive series examining the Machine Learning-as-a-Service offerings from the major cloud providers authored by analyst Janakiram MSV. Automated ML allows you to automate model selection and hyperparameter tuning reducing the time it takes to build machine learning models from weeks or months to days freeing up more time for them to focus on business problems. Create an Azure Machine Learning compute target that will run multiple jobs in parallel to speed up training and hyperparameter tuning Create a training script that would import libraries take user-defined arguments perform data transformation if any tune hyperparameters log metric values etc.

An indiscriminate andor exhaustive hyperparameter search can be computationally expensive and time-consuming. This article describes how to use the Tune Model Hyperparameters module in Azure Machine Learning designer. It has methods for hyperparameter tuning which includes Exhaustive search Heuristic search Bayesian optimization and RL based.

Define the search space Tune hyperparameters by exploring the range of values defined for each hyperparameter. With Azure Machine Learning you can leverage cloud-scale experiments to tune hyperparameters. Increase team efficiency with shared datasets notebooks models and customizable dashboards that track all aspects of the machine learning process.

Depending on the algorithm used you may need to specify hyperparameters to configure how the model is trained. For more information on Azure Machine Learnings hyperparameter tuning offering see the Hyperparameters tuning a model. Operationalize at scale with MLOps.

In this experiment I wanted to try a Machine Learning algorithm. Todays critique looks at Azure Machine Learning services and Google Vertex AI following up on yesterdays post which included the methodology and a review of Amazon Sage Maker. I have chosen Light GBM LGBM for its great performance on different kind of tasks being for instance one of the most used algorithms in Kaggle competitions.

Choosing optimal hyperparameter values for model training can be difficult and usually involved a great deal of trial and error. Classical models used for classification task are statistical models such as Logistic Regression. Hyperparameter tuning aka parameter sweep is a general machine learning technique for finding the optimal hyperparameter values for a given algorithm.

AML provides a hyperparameter tuning service which offers Random Grid and Bayesian parameter sampling supports early termination and manages the jobs creation and monitoring process for the user. Azure Machine Learning enables you to tune the hyperparameters more efficiently for your machine learning models. You can configure a hyperparameter tuning job called a sweep job and submit it via the CLI.

The module builds and tests multiple models by using different combinations of settings. In machine learning models are trained to predict unknown labels for new data based on correlations between known labels and features found in the training data. Basically it is the code that runs on the compute target to train your model.

Azure Machine Learning lets you automate hyperparameter tuning and run experiments in parallel to efficiently optimize hyperparameters.

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