What is AutoML?

Automated Machine Learning, commonly known as AutoML, refers to the process of automating the application of machine learning (ML) techniques to practical challenges. Typically, AutoML encompasses every phase, from handling raw or unclean datasets to constructing a machine learning model that is primed for deployment.

The Current Landscape

As previously highlighted, data scientists often grapple with a myriad of intricate tasks. For instance, the raw data might not be structured in a way that allows for the application of various algorithms. To prepare this data for machine learning, a data expert must implement suitable data preprocessing, feature engineering, extraction, and selection techniques.

Following these preparatory steps, the next challenge involves selecting the right algorithms and optimizing hyperparameters to enhance the model's predictive accuracy. Each of these stages can pose significant challenges, depending on the dataset in question, creating substantial obstacles to leveraging machine learning effectively.

The primary goal of AutoML is to automate and streamline many of these complex steps for data professionals. Given that the intricacies of these tasks often exceed the capabilities of non-experts in ML, the increasing prevalence of machine learning applications has generated a need for easily accessible, off-the-shelf ML solutions that require minimal expertise. Thus, AutoML is the method that automates the labor-intensive, iterative processes involved in developing machine learning models.

Who Can Benefit from AutoML?

AutoML empowers virtually anyone who is not a data scientist to engage in ML projects, as it manages numerous specialized ML tasks and concepts. This capability can significantly reduce the time data scientists spend on routine activities.

Users can train and optimize models without having to deal with hyperparameter selection, cross-validation methods, or specific model architectures (e.g., logistic regression versus decision trees). However, it’s essential to note that the presence of AutoML does not eliminate the need for data scientists in your organization. Their expertise is still crucial for constructing complex models. Future articles will delve deeper into AutoML and explore many fascinating aspects!

List of AutoML Packages for Data Professionals

• AutoGluon: A multi-layer stacking approach that integrates diverse ML models.
• H2O AutoML: Automates model selection and ensembling for the H2O machine learning and data analytics platform.
• MLBoX: An AutoML library featuring three components: preprocessing, optimization, and prediction.
• TPOT: A data science assistant that enhances machine learning pipelines using genetic programming.
• TransmogrifAI: An AutoML library designed to run on top of Spark.