Automated machine learning is a support system for machine learning models to make them more efficient and result-driven.

When ChatGPT was launched in 2022, its developers used automated machine learning to make the language model more efficient and effective. As a result, ChatGPT was able to learn from vast amounts of data, adapt to new tasks, and continuously improve its performance without requiring extensive manual tuning.

Today, we will break down automated machine learning, its features, future prospects, and how they can accelerate the development and deployment of powerful AI models.

Let's begin with the concept of AutoML.

What is AutoML?

When you taste the rice and feel it needs more salt, you might add a pinch or two based on your experience and instinct. But, how do you know the exact amount to add for the perfect flavor?

Similarly, with machine learning models, after they're created, they need tuning to perform better. But, who decides the exact amount of features and values to add or adjust for optimal performance? That's where Automated Machine Learning (AutoML) comes in - it automatically adjusts the "ingredients" (features and values) to make the model perform better, without requiring manual trial and error or exact measurements!

Moving on, we will discuss the features of Automated machine learning.

Features of AutoML

AutoML is packed with a collection of features. Each feature aims to optimize performance, simplify the machine learning process, and improve the results.

Here are 4 major features of AutoML.

• Automated Data Preprocessing

  Manual preprocessing of data includes cleaning, transforming, and preparing data for analysis or modeling, which also involves tasks such as handling missing values, data normalization, feature scaling, and feature selection. It is a time-consuming process therefore AutoML minimizes the potential errors and saves you precious time.

• Automated Feature Selection

  Feature selection is the process of choosing the most important variables (features) from a dataset to use in a machine-learning model.AutoML helps to select the most relevant features.

• Automated Model Selection

  You can understand model selection as a process of choosing the best machine learning model for a specific problem or dataset, from a set of available models, to achieve the highest accuracy or performance.AutoML eases the process of model selection.

• Automated Hyperparameter Turning

  Hyperparameters are like the settings on a camera (e.g. aperture, shutter speed). Adjusting them can improve the picture (model's performance).AutoML picks the best configuration without the need to test and out the best configurations.

Merits of AutoML

• Faster Development: AutoML saves time by automating model selection and hyperparameter tuning.
• Democratization: AutoML makes machine learning accessible to non-experts.
• Better Accuracy: AutoML optimizes model performance.
• Big Data Handling: AutoML handles large datasets efficiently.
• High-Performance Computing: AutoML scales to big data and high-performance computing.
• Industry Agility: AutoML applies to various industries and applications.
• Cost Savings: AutoML reduces labor and expertise costs.
• Error Reduction: AutoML minimizes errors.
• Streamlined Workflow: AutoML streamlines the data pipeline.
• Multimodal Support: AutoML handles diverse data modalities.

Future of Automated Machine Learning

Automated machine learning is a building block for tuning and training the machine learning models. The future of Automated Machine Learning (AutoML) holds a lot of promise. As it continues to improve, AutoML will help us uncover new insights and discoveries, make better decisions, and solve real-world problems more efficiently. By automating the machine learning process, we can focus on tackling complex challenges in healthcare, education, and other vital areas, leading to a positive impact on people's lives