What is AutoML? Discovering its functions, applications, and key advantages

Automated machine learning, also known as AutoML, streamlines machine learning model development process using automated tools and algorithms. It manages the essential steps like data preprocessing, feature engineering, model selection, and hyperparameter tuning.

AutoML systems are designed to extend the boundary of accessibility of machine learning to any non-expert because they automate the repetitive and labor-intensive tasks performed by a data scientist.

Techniques employed include statistical methods, optimization algorithms, and computational intelligence to automatically search and select best models or configurations for a dataset.

Machine learning consulting services help in saving effort and delivering results faster with higher accuracy compared to hand-coded approaches. Thus, more people in your organization can contribute toward building and deploying machine learning models, making AI-driven decision-making more inclusive and efficient.

Want to learn more? What is AutoML? It is a revolutionary method that opens up machine learning to a larger group of people and improves model development efficiency. In this blog, we are going to look at its functionalities, uses, and benefits in depth.

According to Refinitiv AI/ML survey:

Let’s explore more about automated machine learning:

1. Fully automated AutoML

A fully automated AutoML approach is like having a smart assistant do everything for you. You just give the data, and the AutoML platform takes care of all the complicated work – cleaning the data, choosing the appropriate machine learning model to be used, and determining the best configurations for that model.

2. Model-based AutoML

Model-based AutoML can be compared as having an expert model builder in your pocket. It will assist you in picking the best available machine learning model and its suitable settings to have the most accurate predictions. In other words, it is just like a model recommendation system.

3. Pipeline-based AutoML

Pipeline-based AutoML is a customizable machine learning assembly line. It automates the entire model-building process, from preparing the data to making predictions.

4. Task-specific AutoML

Task-specific AutoML is akin to having specialized experts for a particular machine learning task. It is a customized solution for your unique business requirements.

What are the functions of AutoML?

1. Data preprocessing

Data preprocessing is a crucial part of machine learning where data is cleaned and prepared before training a model. AutoML platforms perform all the steps from managing missing values, categorical variables, and normalizing data. The analysis is carried out automatically, and proper preprocessing is performed to make sure data is in a suitable format for model training.

2. Feature engineering

Feature engineering transforms raw data into meaningful and predictive features to augment the functioning of ML models. AutoML platforms present automated feature engineering abilities, which automatically create and choose appropriate features from the data.

3. Model selection

Choosing an appropriate machine learning model for a problem is difficult because each model has its strength and weakness. Automating this process, AutoML evaluates several models simultaneously for accuracy based on predefined assessment metrics.

4. Hyperparameter tuning

Machine learning models have hyperparameters that control their actions and performance. The right combination of hyperparameters is very important to achieve optimal model performance. AutoML automates the process of hyperparameter optimization by automatically exploring through a scale of hyperparameter values and choosing the best ones.

5. Model evaluation

After the model is trained and optimized, it needs to be evaluated for its execution and generalization. AutoML platforms offer automated model evaluation functions, which calculate different evaluation metrics and generate performance reports.

6. Ensembling and stacking

Ensembling is the method of combining more than one model to make predictions for improving performance. AutoML platforms offer two types of functionalities: ensembling and stacking, which combine multiple models to produce a more robust and accurate ensemble model.

7. Automation reporting and documentation

AutoML platforms always offer automated reporting and documentation abilities. These kinds of features provide users with capabilities to track entire machine learning pipelines, including how data is preprocessed, feature engineered, and all other such stages.

8. Deployment and production

After a model is trained and tested, it is deployed to a production environment where it will be applied for prediction on new data. AutoML platforms also provide functionalities for deploying and operationalizing machine learning models in a production-ready approach.

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Business applications of AutoML

1. Healthcare

AutoML enhances diagnostics and treatment recommendations. Google AutoML, for instance, has been used to develop ML models to detect diabetic retinopathy, helping doctors intervene early and improve patient outcomes.

2. Financial services

AutoML strengthens fraud detection and risk assessment. PayPal utilizes AutoML to build ML models that analyze transaction patterns, identifying fraudulent activities and protecting users from financial losses.

3. Manufacturing

AutoML optimizes production processes and reduces downtime. Bosch leverages AutoML to predict machine failures, improving maintenance scheduling and operational efficiency.

4. Retail & e-commerce

AutoML enhances personalization and demand forecasting. Stitch Fix uses AutoML to provide AI-driven fashion recommendations, boosting customer engagement and sales.

5. Transportation & logistics

AutoML optimizes delivery routes and fleet management. UPS uses AutoML to improve logistics, minimizing fuel consumption and enhancing delivery efficiency.

6. Energy & utilities

AutoML drives smart energy management and predictive maintenance. E.ON utilizes AutoML to optimize wind turbine operations, maximizing energy efficiency and output.

48% of businesses globally use machine learning.
– DemandSage

Top AutoML tools and solutions

Google Cloud AutoML

Google Cloud AutoML is a comprehensive suite of machine learning tools designed to simplify the development, training, and deployment of AI models.

Auto-Sklearn

Auto-Sklearn is an open-source automated machine learning (AutoML) library built on scikit-learn. It enhances the efficiency of ML workflows.

AutoKeras

Based on Keras, AutoKeras is an open-source Python library for AutoML. It automatically searches for the best neural network architecture. It streamlines model development for companies without needing knowledge in neural network architecture.

Amazon Lex

Amazon Lex, powered by AWS, provides businesses with an advanced natural language interface to build chatbots and conversational AI solutions. It leverages the same AI technology as Amazon Alexa.

H2O AutoML

H2O AutoML, developed by H2O.ai, is a powerful suite of machine learning solutions that streamline model training and deployment across various use cases. This platform is particularly valuable for businesses looking to automate complex AI workflows.

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Key advantages of AutoML

• Enhanced efficiency – Speeds up and simplifies machine learning lifecycle, reducing model training time.
• Optimized performance – Delivers high-quality models with automated fine-tuning and optimization.
• Cost savings – AutoML decreases operational costs by automating resource-intensive machine learning tasks.
• Democratization of AI – Enables users to build and deploy powerful machine learning models even without deep AI expertise.

Embracing AutoML: The future of AI-driven innovation

Automated machine learning will redefine AI-powered innovation across sectors. With a growth spurt in the adoption of machine learning, AutoML will be an enabler towards the ease of incorporating artificial intelligence while maximizing decision-making and fueling business growth.

Organizations can look forward to:

• Faster, more scalable AI development
• Greater accessibility to AI-powered analytics
• Increased innovation across industry verticals

AutoML helps businesses unlock new opportunities, foster a data-driven culture, and gain a competitive edge in an AI-first world. For more information on how automated machine learning development services can elevate your business, talk to our AI experts.