Today it is hard to imagine a business that doesn’t use machine learning in analytics and decision-making. However, the development and deployment of ML models can be very complicated and resource-consuming. Even experienced data scientists and ML engineers could benefit from effective automation.
AutoML is a way to automate machine learning in your enterprise. In this article, we will talk about the benefits of AutoML, its operating principles, and ways to use it in your business.
Definition of AutoML
AutoML, short for Automated Machine Learning, is the process of automating the end-to-end process of applying machine learning to real-world problems. Typically, developing and deploying machine learning models takes a lot of time and effort. ML engineering is required to match the right model with the right business task, train it, fine-tune the parameters, and so on.
AutoML for classical machine learning models (for example, decision trees, random forests, support vector machines) and AutoML for neural networks (deep learning models) are quite different in terms of approach, costs, and use cases.
Classical machine learning models are often less complex and require fewer parameters to tune, while neural networks, especially deep learning models, are highly complex with many layers, neurons, and parameters (weights, biases, activations) that need to be optimized. They require significantly more computational power and time for training, particularly when dealing with large datasets like images, video, and text.
In this article, when we talk about ML we mainly focus on neural networks.
To illustrate how AutoML today may look in an enterprise, imagine a large telecommunications company that is experiencing a high churn rate. They have a lot of data, including customer demographics, billing history, service usage patterns, customer support interactions, and past churn data.
The AutoML platform ingests this data and automatically cleans it: handles missing values, normalizes data, and encodes categorical variables like customer location or type of service plan.
Then it can identify the most relevant features for predicting churn, such as the frequency of customer service calls, the length of time a customer has been with the company, and recent changes in service plans.
The platform may also create new features, like combining multiple service usage metrics into a single feature representing overall engagement. All this enables the company to drive necessary insights from data that can be used for performance improvement even if they don’t have a specialized ML or data science department in-house.
The difference between AutoML and traditional machine learning (ML) is in the level of automation, expertise required, and the overall process involved in developing and deploying ML models.
The goal of AutoML is to one day make machine learning accessible to a broader audience, including those with limited expertise in data science. However, today this tool should be considered more as an assistant to professionals. It still requires understanding of how machine learning works and what decisions should be made for optimal performance.
How does AutoML work?
To work with an AutoML tool you need at least some knowledge of a programming language such as Python. Many tools offer a graphical interface, however, it might not be suitable to perform all the tasks and some coding will still be required.
Here is how you can get started with AutoML.
Prepare a dataset
To use AutoML, you need to provide the model with your data. It can be transaction history, sales data, patient medical data, or else. Usually, you can upload the data that you have in the UI, in a CSV or JSON format.
While AutoML tools can automatically clean data and transform raw data into formats that are more suitable for ML models, the accuracy of this transformation might be compromised. Quality insights always start with quality data.
Select features
You need to set the objective of model training, for example, classification or regression. You can follow our guide that explains how to choose the right machine learning technique depending on your task.
AutoML algorithms will automatically select the most relevant features from the raw data that best fits the objective.
For example, imagine you want to predict whether a customer will make a purchase (Yes/No) based on their profile features. This is your dataset. It has many features, some of which are relevant:
After running the AutoML process, the algorithm might determine that the most relevant features for predicting a purchase are:
- Previous purchases. Customers with a higher number of previous purchases are more likely to make a purchase.
- Browsing time. More time spent browsing often correlates with higher purchase intent.
- Preferred category. Certain categories may have a higher likelihood of leading to a purchase.
- Income. Higher income might influence the purchasing ability.
The reduced dataset will look something like this:
Choose model
AutoML platforms automatically evaluate multiple algorithms to identify the one that best fits the data. This involves comparing a wide range of models, such as decision trees, random forests, gradient boosting machines, and neural networks, among others.
For example, when predicting customer purchases, AutoML may consider several algorithms available on the platform such as logistic regression, random forest or XGBoost and determine that XGBoost will be the optimal choice because it effectively captures non-linear relationships and interactions between features.
Optimize hyperparameters
Hyperparameters are the settings that control the behavior of a machine learning algorithm. AutoML tools automatically tune these hyperparameters to optimize model performance, which traditionally requires considerable manual experimentation.
Train model
Once the best model and hyperparameters are selected, AutoML platforms train the model on the provided dataset. They also automatically split the data into training, validation, and test sets to ensure that the model is evaluated fairly and is generalizable to unseen data.
You can train several models on your data and compare their performance or just use the platform to enhance traditional ML approach.
AutoML solutions
AutoML has gained significant traction, and several leading tech companies provide AutoML solutions as part of their broader machine learning and AI platforms.
Databricks AutoML
Databricks offers AutoML as part of its machine learning toolkit. Databricks AutoML simplifies the process of building and deploying machine learning models by automating many of the time-consuming and complex tasks involved.
Notebook integration. AutoML on Databricks is integrated into the Databricks notebooks, allowing you to use the familiar notebook interface to generate and refine ML models.
Support for Python and Scala. It supports Python and Scala, making it accessible to data scientists and engineers familiar with these languages.
Hyperparameter tuning. Databricks AutoML automatically tunes hyperparameters to optimize model performance, saving time and effort.
Explainability. It offers tools to help interpret models, enabling you to better understand how decisions are made by the models.
Google Cloud AutoML
Google Cloud AutoML is a suite of machine learning products that enables developers with limited ML expertise to train high-quality models tailored to their specific needs. Google Cloud offers AutoML for various types of data, including images, text, video, and tabular data.
AutoML Vision, AutoML Natural Language, and AutoML tables. Google provides specialized AutoML products for different data types, allowing users to create custom models for image classification, sentiment analysis, and structured data prediction without needing in-depth ML knowledge.
Transfer learning. Google Cloud AutoML often uses transfer learning, where pre-trained models are fine-tuned on the user’s specific dataset, speeding up the process and improving performance.
Scalability. Built on the Google Cloud infrastructure, AutoML scales to handle large datasets and complex models, making it suitable for enterprises of all sizes.
Integration with Google Cloud Ecosystem. AutoML is integrated with other Google Cloud services, such as BigQuery and Google Data Studio, providing a comprehensive data-to-insight pipeline.
Azure AutoML
Azure AutoML is designed to simplify machine learning for data scientists, engineers, and business analysts by automating the complex and time-consuming tasks involved in the ML lifecycle. Azure AutoML is a part of Azure Machine Learning, a cloud-based service that provides a complete platform for developing, deploying, and managing machine learning models.
Integration with Python SDK. Advanced users can interact with Azure AutoML via the Python SDK, allowing for customizations and integration into existing workflows.
Cloud scalability. By utilizing Azure’s cloud infrastructure, AutoML can scale to handle large datasets and complex models, ensuring that the solution can grow with the business needs.
Integration with Azure Ecosystem. Azure AutoML integrates with other Azure services, such as Azure Data Lake, Azure Databricks, Azure SQL Database, and Azure DevOps, enabling a smooth end-to-end ML pipeline.
Notebooks. Data scientists can also use Jupyter notebooks within Azure Machine Learning for a more hands-on approach, leveraging the full power of Python and Azure AutoML’s APIs.
AutoML libraries
If you’re not ready to commit to a full-fledged platform or require more flexibility than they can offer, there are several highly regarded AutoML libraries that cater to different use cases:
1. TPOT (Tree-based Pipeline Optimization Tool)
TPOT is an open-source AutoML library in Python that optimizes machine learning pipelines using genetic programming. It automatically explores different combinations of algorithms and preprocessing steps to find the best model pipeline.
TPOT is commonly used for tasks like classification and regression, especially in research and experimentation.
- Strengths: Good for users looking for flexibility and control over the evolution process.
- Limitations: May not scale well for very large datasets.
2. Auto-sklearn
Auto-sklearn is an open-source AutoML tool built on top of the popular Scikit-learn library. It automatically searches for the best machine learning model for a given dataset by using a combination of meta-learning and Bayesian optimization.
It is commonly used for structured data tasks like classification and regression, for example, in academic research and enterprise applications.
- Strengths: Excellent integration with Scikit-learn, fast and efficient, and easy to implement for users already familiar with Scikit-learn.
- Limitations: Not suitable for very large datasets or complex, custom pipelines.
3. FLAML (Fast and Lightweight AutoML)
FLAML is a fast, lightweight AutoML library designed for efficiency and simplicity, making it ideal for small to medium-sized datasets and limited resources. It is fast and resource-efficient, making it suitable for time-sensitive or resource-constrained environments.
Small businesses or developers looking for quick, efficient AutoML solutions without needing large-scale infrastructure can benefit from using this library.
- Strengths: Fast, lightweight, and ideal for small to medium datasets, integrates with Scikit-learn.
- Limitations: Less feature-rich than some other AutoML platforms, not ideal for large datasets or very complex tasks.
4. MLJAR AutoML
MLJAR AutoML is an open-source tool designed for easy model building and performance benchmarking, primarily focused on structured data (classification and regression tasks).
Provides a comprehensive report that includes detailed model performance metrics, visualizations, and model explanations.
Offers functionality for hyperparameter tuning, feature engineering, and automatic ensembling. Ideal for generating fast, interpretable models for structured data, for example, in healthcare.
- Strengths: Strong focus on model interpretability, easy to use, and ideal for structured data.
- Limitations: Limited to structured data, less flexible for custom workflows.
Use cases for AutoML
The adoption of AutoML is growing across various sectors. Here are some industries that commonly use AutoML:
Business intelligence and analytics
AutoML can be used to enhance traditional business intelligence tools by enabling automated predictive analytics. For instance, sales forecasting, customer segmentation, and churn prediction can all be improved with minimal manual intervention.
Healthcare
In healthcare, AutoML is employed to develop models for diagnosing diseases, predicting patient outcomes, and optimizing treatment plans. By automating these processes, healthcare providers can make more accurate and faster decisions.
Finance
Financial institutions employ AutoML for credit scoring, fraud detection, and algorithmic trading. By automating model development, these institutions can quickly adapt to changing market conditions and emerging threats.
Retail and e-commerce
AutoML can personalize shopping experiences by predicting customer preferences, optimizing pricing strategies, and improving inventory management. This enables retailers to enhance customer satisfaction and operational efficiency.
Manufacturing
In manufacturing, AutoML is used for predictive maintenance, quality control, and supply chain optimization. By analyzing sensor data and historical records, AutoML models can predict equipment failures and optimize production schedules.
Benefits of AutoML
Adopting AutoML in software engineering and business contexts provides a number of advantages:
- Scalability. AutoML allows organizations to scale their machine learning efforts without a proportional increase in data science resources.
- Efficiency. By automating repetitive and time-consuming tasks, AutoML reduces the time it takes to develop and deploy models.
- Accessibility AutoML bridges the gap between business professionals and machine learning, enabling non-experts to use advanced analytics for decision-making.
- Cost-effectiveness. By reducing the need for specialized data science expertise and minimizing the trial-and-error phase of model development, AutoML can significantly lower the costs associated with machine learning projects.
Challenges
While AutoML offers numerous benefits, there are also challenges connected to its adoption:
- Interpretability. AutoML often prioritizes performance over interpretability, leading to the development of “black-box” models that are difficult to understand and explain.
- Customization. AutoML platforms may not always offer the flexibility needed for highly specialized use cases.
- Data quality. The effectiveness of AutoML is contingent on the quality of the input data. Poor data quality can lead to suboptimal models.
Summing up
AutoML represents a significant leap forward in the democratization of machine learning. By automating the complex and technical aspects of ML, it empowers organizations to benefit from the full potential of their data. However, it is not a silver bullet to solve all the problems. There are also challenges connected to the adoption and use of AutoML such as low interpretability and customization.
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