Create Machine Learning model without code in Azure AutoML

Automated machine learning (AutoML) ?

Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:

Preprocess and clean the data.
Select and construct appropriate features.
Select an appropriate model family.
Optimize model hyperparameters.
Postprocess machine learning models.
Critically analyze the results obtained.

As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.

Lets jump to Azure AutoML

In recently Microsoft launches Azure AutoML service to Empower professional and non-professional data scientists to build machine learning models rapidly. Automate time-consuming and iterative tasks of model development using breakthrough research—and accelerate time to market.

Automated ML is now in preview, accessible through the Azure Machine Learning service. Automated ML empowers customers, with or without data science expertise, to identify an end-to-end machine learning pipeline for any problem, achieving higher accuracy while spending far less of their time. And it enables a significantly larger number of experiments to be run, resulting in faster iteration towards production-ready intelligent experiences.

Features of Azure AutoML

Automatically build and deploy predictive models using the no-code UI or through a code-first notebooks experience.
Increase productivity with easy data exploration and profiling and with intelligent feature engineering.
Easily create accurate models customized to your data and refined by a wide array of algorithms and hyperparameters.
Build responsible AI solutions with model interpretability, and fine-tune your models to improve accuracy.

How AutoML works ?

In Azure AutoML during model training, Azure Machine Learning creates a number of in parallel pipelines that try different algorithms and parameters. 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. The following diagram illustrates this process.

In Azure AutoML there are special data automatic data preprocessing , its automate machine learning model, your data is automatically scaled or normalized to help algorithms perform well.

one of the following scaling or normalization techniques will be applied to each model.