AutoML Solutions: How Can They Transform Your Business?

Hrvoje Smolic

AutoML Solutions

Artificial intelligence (AI) is continuously evolving, and its applications seem to expand exponentially over the years. It is a rich field that holds tremendous potential across all industries, and further technological advancements only promise so much more for the future. 

One of the most recent evolutions in AI is automated machine learning (or automl solutions), which is leveraged to help machines learn and improve their abilities

This article discusses automated machine learning, how it works, and some of its benefits. 

We also explore some challenges in automating the machine learning process.

What are AutoML solutions?

Automated machine learning (or automl) uses algorithms to automatically find and use the best possible models for predictive modeling tasks. 

It is a branch of artificial intelligence that has become increasingly popular in recent years. Companies seek to reduce the costs and time associated with traditional machine learning techniques.

Data scientists can use automated machine learning algorithms for various tasks, including 

  • regression, 
  • classification, 
  • timeseries forecasting
  • and clustering.

In general, automl solutions and algorithms can identify relationships between different variables in data sets and use those relationships to predict future events or observations. 

Yes, automated machine learning is still in its early stages of development. But it has shown great promise as a cost-effective and efficient prediction method in real-life business scenarios.

automl solutions
Standard machine learning vs AutoML solutions workflow

What Steps Are Involved in AutoML?

There has been a growing interest in automated machine learning in recent years. The field aims to develop algorithms that automatically select and apply the best machine learning models for a given data set. 

This will save a lot of time and effort for analysts and data scientists, who would otherwise need to spend many hours or days tuning parameters and trying different models. 

There are many different approaches to AutoML, but most of them follow similar steps:


AutoML algorithms typically begin with preprocessing of the data to make it easier to work with. This may involve 

  • scaling numerical features or 
  • one-hot encoding categorical features.

Feature Engineering

After the data has been preprocessed, the next step is to engineer features (columns in the data set). This step may involve creating new features from existing ones or selecting a subset of features to use in the model.

Feature engineering is one of the most critical steps in any machine learning project. It takes raw data and transforms it into features that a machine learning algorithm can use. 

autoML solutions features
Image by the Author: autoML solutions features

Feature engineering can be a time-consuming and tedious task, but it is essential for getting good results.

Data scientists use many techniques for feature engineering, and the best approach will vary depending on the data and the problem you are trying to solve. Traditional methods include 

  • feature selection, 
  • feature extraction, 
  • and dimensionality reduction. 

By carefully engineering your features, you can significantly improve the performance of your machine learning models.

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Model Selection and Tuning

Selecting the proper model is essential to the success of the AutoML process. Once the appropriate model has been chosen, tuning it involves selecting the correct hyperparameters for the model and training it on the data. 

Tuning is a critical step for automated machine learning. It is often necessary to experiment with different models and hyperparameters to find the best-performing ones, which is very time-consuming.

There are several ways to tune a machine learning model, but cross-validation is the most common method. It involves dividing your data into several parts and training the model on each part, one by one. The goal is to find the combination of parameters that results in the best performance on the test set. 

Another popular method for model selection and tuning is grid search. This approach involves systematically trying different combinations of parameters and selecting the one that produces the best results. 

Cross-validation and grid search can be incredibly time-consuming, so choosing the correct method for your particular problem is essential for an efficient process.

autoML solutions model training
Image by the Author: autoML solutions model training in Graphite Note

Model Training

Data is prepared and fed into the algorithms in the model training stage. The aim is to find the best way to map the input data to the output desired results. This process can be time-consuming and resource-intensive, but it is essential to ensure that the final model is accurate and reliable.

Data scientists use different techniques in model training, and AutoML systems often use various methods to find the most effective solution. Once the model has been trained, it can be deployed to provide predictions on new data.

Model Deployment

Model deployment is the process of taking a trained machine learning model and making it available for use in production environments. This stage can be incredibly challenging because it requires putting the model into production and ensuring it performs as expected. 

Deployment is often the most complicated step in the machine learning process. It frequently requires a lot of time and energy from data scientists and programmers. 

Once all adjustments are made and the model is ready for deployment, it can help companies make data-centric decisions for their benefit.

autoML solutions pic

What Are the Benefits of AutoML solutions?

AutoML solutions are a branch of machine learning that deals with the automated development of models that can be used for predictive data analysis. 

In other words, it is a method of training algorithms to optimize themselves through experience. 

By automatically constructing and tuning models, AutoML can help companies with the following:

  1. AutoML can automate the training and testing of machine learning models, saving a ton of time and money. Furthermore, it can improve the accuracy of results by reducing the number of parameters that need to be tuned.
  2. AutoML can help organizations to get their products and services to market faster and more efficiently. 
  3. AutoML can also improve the overall accuracy of models by providing more robust data mining capabilities and increased transparency throughout the model development process. 

Do You Need AutoML solutions?

Automated machine learning opens up new possibilities for AI and will speed up the process of teaching machines. 

As automated machine learning becomes increasingly sophisticated, its use will likely become more widespread. Businesses should start preparing to take advantage of this technology. 

No-code machine learning platforms like Graphite Note empower business users to generate no-code machine learning models - without writing a single line of code.

It's everything you need to bring your data into the 21st century and supercharge decision-making.

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