What is AutoML?

Automated Machine learning, or AutoML, is an approach to build machine learning models. It automates the entire process of model development tasks, unlike the traditional method where tasks are done manually.

AutoML empowers users ,from data scientists and ML engineers to business owners and stakeholder of varying skill levels, to build quality models without requiring any programming expertise or statistical knowledge by. This approach accelerates the time to take the model from idea to production in a short span, unlike the traditional process.

Behind the Scenes of an AutoML Pipeline

AutoML includes tasks like data-preprocessing, feature engineering, algorithm selection, hyperparameter tuning and model evaluation, which are done in one go without any manual intervention. It performs the essential operations sequentially according to optimize and make the input data ready by itself and executes the tasks automatically until the final goal, a high performing model, is built.

General stages of machine learning model development which are automatically handled by AutoML are as follows:

1. Data Preprocessing - The first step AutoML performs is to clean and transform the raw data into usable format. It automatically addresses the missing values, outliers, encoding, and data normalization to optimize the data for model input.
2. Feature Engineering - AutoML reduces the size of the data and complexity by reducing the noise and unwanted features or creates new features when necessary to help improve the model performance and accuracy. Using important and most relevant features in model building results in a high performing model.
3. Model Selection - Various algorithms are tested for better performance that suits the dataset - to find the best fit. AutoML compares and identifies the most suitable model based on its performance.
4. Hyperparameter Tuning - AutoML also performs the task that is the heart of building a high performing model, hyperparameter tuning, to find the most effective combination. It applies multiple techniques like grid search, random search, or Bayesian optimization to improve model accuracy and reduce error rates.
5. Model Evaluation - Different evaluation metrics are being used for different models to evaluate the performance. After tuning, the model undergoes evaluation using a validation dataset, where key metrics like accuracy, precision, recall, F1 score, or mean squared error are evaluated. This stage helps determine the model’s quality and performance.
6. Ensemble Methods - AutoML also uses ensemble techniques, if required, combining multiple models to boost accuracy and performance. These methods can increase accuracy by blending the strengths of different algorithms.

AutoML in QuickML

QuickML’s AutoML feature automatically generates a complete pipeline with essential stages, offering an end-to-end solution that mirrors the custom pipeline building mode. Currently, AutoML in QuickML only supports prediction models, enabling users to build various types of Classification, Regression, and Ensemble models tailored to the input data and specific business requirements.

This streamlined AutoML process simplifies model development while ensuring high-quality, data-driven predictions, making it an effective tool for users looking to accelerate their model-building workflow.

Key benefits of QuickML’s AutoML

QuickML offers significant advantages with its AutoML capabilities, providing streamlines and accessible approach to build machine learning models.

• Easy interpretation of pipeline stages
• Triggering AutoML in a few clicks making it user-friendly for non-experts
• Reduced resource dependency
• Editable pipelines for optimization
• Seamless endpoint creation for real-time predictions
• Consistency and reliability in model quality
• Time and cost efficiency
• Scalable and adaptable for various use cases (Classification, Regression, Ensemble models)