AutoML: Importance, Benefits Challenges & Software [2025]

Automated machine learning (AutoML) has the potential to increase the productivity of data scientists and machine learning tools. As the need for data scientists is increasing, AutoML tools/services become more popular and help companies use machine learning successfully to extract business insights in an effective and scalable manner.¹ We explore AutoML, its importance, challenges and tools.

What is automated machine learning?

Automated Machine Learning (AutoML) is the process of automating the end-to-end tasks of applying machine learning to real-world problems. AutoML simplifies the workflow by automating steps that traditionally require deep expertise in data science and machine learning, making it accessible to non-experts while also improving efficiency for experts.

AutoML services aim to automate some or all steps of the machine learning process which includes:

Since accuracy of machine learning solutions can be measured, automated systems can fine-tune data, features, algorithms and hyperparameters of algorithms to generate accurate models relying on established machine learning knowledge and trial-and-error.

Refer to Figure 1 to see a leading AutoML vendor’s explanation. Areas highlighted in gray illustrate which parts of the machine learning process are automated via autoML.

Figure 1: Parts of Machine Learning processes automated via AutoML

Source: DataRobot ²

Why is it important now?

Need for more data scientists

As data science becomes a more integrated part of our lives, businesses need more solutions in this field and demand more data scientists to build these solutions. Without data science methods, companies might be unable to understand their processes, monitor performance levels, or take certain actions to prevent huge losses. 

According to the U.S. Bureau of Labor Statistics (BLS), data scientist jobs will grow by 36% from 2021 to 2031 while the average growth rate for all occupations is 5%. ³ Considering the scarcity of data scientists and the amount of time for building data science solutions, autoML solutions can help businesses satisfy their demand for data scientists.⁴

Errors in applying machine learning algorithms

It is up to data scientists to implement machine learning algorithms and choose a method that works best for the business case. However, the implementation process is prone to human made errors and bias. AutoML tools can automate this process and also run a broader set of machine learning algorithms to select the best one, which might not be considered by data scientists before. 

Today, Facebook trains around 300,000 machine learning models to improve its machine learning processes and even created its AutoML engineer named Asimo to generate improved versions of existing models automatically. ⁵

As these capabilities will accelerate machine learning processes, autoML solutions will improve the return on investment (ROI) of machine learning projects.

AutoAI vs AutoML

There is no strict distinction between AutoAI and AutoML. Some vendors explain automated AI as a variant of AutoML that uses intelligent automation to automate tasks throughout the entire lifecycle of machine learning (ML) and artificial intelligence models.

On the other hand, there may be AutoML tools that use intelligent automation and automate as many tasks as possible within the ML lifecycle. So, if you come across different tools labeled as AutoML or AutoAI, you should check:

What are the benefits of AutoML?

1. Accessibility for Non-Experts

• Democratizes Machine Learning: AutoML makes machine learning accessible to non-experts, such as business analysts, engineers, or decision-makers who lack in-depth knowledge of data science or coding. It allows them to create and deploy machine learning models easily.
• Low Barrier to Entry: With AutoML, even users with minimal machine learning experience can develop effective models, broadening the potential user base and helping organizations implement data-driven decision-making across more departments.

2. Increased Productivity for Experts

• Saves Time: By automating the repetitive and time-consuming aspects of the machine learning pipeline, such as data preprocessing, model selection, and hyperparameter tuning, AutoML enables data scientists to focus on more strategic tasks like problem formulation, interpretability, and addressing business-specific needs.
• Streamlines Workflow: AutoML accelerates model development by automating tedious steps, which helps data scientists and engineers iterate faster and experiment with more model configurations in less time.

3. Optimized Model Performance

• Automated Hyperparameter Tuning: AutoML automatically performs complex hyperparameter tuning, often resulting in models that are fine-tuned to achieve better performance metrics (e.g., accuracy, precision, F1-score) than manually configured models.
• Ensemble Models: Many AutoML frameworks automatically build and combine multiple models (e.g., via stacking or bagging) to create highly accurate and robust predictive models.

4. Consistency and Reduced Human Error

• Standardized Workflows: Automation ensures that critical machine learning tasks follow best practices and standardized methodologies, reducing the chance of human error in model development and tuning.
• Reliable Output: AutoML tools consistently perform tasks like feature engineering, model selection, and evaluation, leading to more reliable and reproducible results.

5. Exploration of Multiple Models

• Automated Model Selection: AutoML can try out a wide range of machine learning algorithms and approaches, from simple models like decision trees to more complex ones like neural networks, ensuring that the best model for the task is chosen.
• Rapid Experimentation: Users can quickly experiment with different datasets, algorithms, and configurations without needing to manually test each model, which encourages innovation and exploration of new ideas.

6. Cost and Resource Efficiency

• Reduced Need for Specialized Expertise: Organizations that may not have the budget or resources to hire a team of machine learning experts can still leverage advanced machine learning models through AutoML.
• Faster Time to Market: AutoML can drastically reduce the time it takes to go from data collection to model deployment, helping businesses get insights and products to market more quickly.

7. Better Scalability

• Automated Model Deployment: Some AutoML systems not only create models but also streamline their deployment into production environments, ensuring scalability and continuous improvements with less effort.
• Adapts to Larger and Complex Datasets: AutoML can handle large-scale datasets and complex problems, making it easier to scale machine learning applications as data grows.

8. Improved Interpretability (in some tools)

• Model Transparency: Some AutoML frameworks provide interpretable models or explainability tools that help users understand how decisions are being made by the model, which is critical for fields like healthcare, finance, and regulatory industries.
• Feature Importance: AutoML tools often automatically identify and rank important features, providing insights into the key drivers of model predictions.

9. Robustness to Data Quality Issues

• Handling Missing or Noisy Data: AutoML tools can automate preprocessing tasks, handling missing values, outliers, and noisy data with minimal human intervention, which improves model performance on real-world data.

10. Rapid Prototyping

• Quickly Build Baseline Models: AutoML allows for the rapid generation of baseline models, which can be used to gauge the feasibility of machine learning approaches for specific problems. Even if further manual tuning is needed, this quick start can be highly valuable for experimentation.

Why Do We Still Need Data Scientists in the Age of AutoML?

AutoML tools have come a long way. Many are now capable of handling more complex tasks like:

• Custom Model Constraints: Some AutoML platforms are better at optimizing for constraints like resource limitations, making them more viable for edge deployment scenarios.
• Explainability: Explainable AI (XAI) is now a major focus in AutoML development. More platforms offer transparency tools that allow for a clearer understanding of model decisions, which was previously a strong advantage for data scientists.

Additionally, AutoML tools today can deliver near-human-level performance on many standard tasks. While they might still struggle with highly complex competition-level problems (like on Kaggle), they have become quite capable for a wide range of business applications.

Evolving Role of Data Scientists

However, the role of data scientists isn’t disappearing. Instead, it’s evolving:

1. Higher-Level Focus: As AutoML takes over routine model-building tasks, data scientists are increasingly focusing on strategic decision-making, ethical concerns, and alignment of models with long-term business goals. The tasks that involve business acumen, data governance, and cross-functional collaboration remain crucial.
2. Specialized Applications: In niche areas like deep learning, complex forecasting, or custom NLP models, data scientists still outperform AutoML tools. These areas require creative problem-solving and deep technical expertise that AutoML cannot yet fully automate.
3. Integration and Deployment: Data scientists also excel in ensuring that machine learning models fit within the organization’s existing infrastructure and strategic vision, which AutoML cannot yet fully account for.

More Automation, More Collaboration

What’s becoming clear is that AutoML and data scientists are not competitors; they are complementary. AutoML automates routine tasks, speeding up the model development process, while data scientists handle more nuanced, creative, and strategic aspects of machine learning.

Non-experts are now able to use AutoML for many use cases, but expert oversight is still critical in areas like ensuring fairness, addressing ethical concerns, or working on cutting-edge models.

The Path Forward

In short, the predictions from earlier years are coming true: the basics of data science are being democratized, thanks to AutoML. More people in business and technical roles are able to work with machine learning. But data scientists continue to lead on more complex, customized, and strategic tasks. The trend is toward greater collaboration between AutoML tools and human expertise, rather than one replacing the other. data scientists will still be needed for more complex, strategic tasks.

What are the AutoML tools?

1. Google Cloud AutoML

Google Cloud AutoML is a cloud-based tool designed to make machine learning accessible to businesses with limited data science expertise. It offers pre-trained models for tasks like image recognition, language processing, and structured data. The tool also integrates seamlessly with Google Cloud, offering a powerful, scalable solution for enterprises.

• Platform: Cloud-based
• Use Case: Best for businesses needing scalable, cloud-based machine learning solutions with minimal setup.

2. H2O.ai (H2O Driverless AI)

H2O.ai’s AutoML platform is known for its powerful feature engineering, automatic model selection, and interpretability tools. It supports both simple and complex machine learning tasks, including time series and NLP, and is popular in industries like finance, healthcare, and marketing.

• Platform: Cloud, on-premise, and hybrid
• Use Case: Ideal for industries needing high model accuracy, explainability, and time series analysis.

3. Microsoft Azure AutoML

Azure AutoML is integrated within Microsoft’s Azure ecosystem, making it ideal for users who already use Azure for cloud computing. The tool supports a wide variety of algorithms and offers seamless deployment and management for machine learning models.

• Platform: Cloud-based (Azure)
• Use Case: Best for organizations using Microsoft Azure and seeking cloud-based AutoML for easy integration and deployment.

4. Auto-sklearn

Auto-sklearn is an open-source Python library built on scikit-learn, automating model selection, hyperparameter tuning, and ensemble creation. It’s highly flexible for developers and data scientists working in Python environments.

• Platform: Open-source (Python library)
• Use Case: Suitable for developers looking for a customizable AutoML tool within a Python framework.

5. TPOT (Tree-based Pipeline Optimization Tool)

TPOT is an open-source Python library that uses genetic algorithms to optimize machine learning pipelines. It automates feature engineering and model selection, creating robust pipelines for a variety of use cases.

• Platform: Open-source (Python library)
• Use Case: Great for users wanting to automate machine learning pipelines and maximize performance through genetic algorithms.

6. Amazon SageMaker Autopilot

Amazon SageMaker Autopilot is part of AWS and allows users to build and deploy machine learning models with minimal effort. It automatically generates multiple models and provides transparency into how they were selected, making it ideal for businesses already using AWS.

• Platform: Cloud-based (AWS)
• Use Case: Perfect for businesses leveraging AWS infrastructure for machine learning and cloud computing.

7. BigML

BigML offers a highly visual, user-friendly interface for building machine learning models without coding. It supports tasks like classification, regression, time series forecasting, and anomaly detection, making it an excellent choice for users who prefer a no-code experience.

• Platform: Cloud-based
• Use Case: Ideal for non-technical users or teams needing an intuitive, visual interface for machine learning.

8. DataRobot

DataRobot is an enterprise-grade AutoML platform that excels at building, deploying, and monitoring machine learning models. It is known for its advanced interpretability tools, making it popular among businesses in regulated industries such as healthcare and finance.

• Platform: Cloud, on-premise, and hybrid
• Use Case: Best for enterprises requiring robust AutoML with deep insights and regulatory compliance.

9. Ludwig (by Uber)

Ludwig is an open-source deep learning AutoML tool developed by Uber. It allows users to build machine learning models without writing code, making it accessible to non-programmers while still offering deep learning capabilities for more advanced use cases.

• Platform: Open-source (Python library)
• Use Case: Great for users looking for no-code deep learning solutions and ease of use in a variety of tasks.

10. AutoKeras

AutoKeras is an open-source AutoML tool built on top of Keras and TensorFlow. It automates the process of creating deep learning models, making it easier for developers to work with complex data like images and text without deep expertise in neural networks.

• Platform: Open-source (Python library)
• Use Case: Ideal for developers needing to automate deep learning model creation within TensorFlow/Keras.

11. RapidMiner

RapidMiner offers a drag-and-drop interface for creating machine learning workflows, making it user-friendly for non-technical users. It automates much of the data preparation and model selection processes, requiring little to no coding.

• Platform: Cloud and on-premise
• Use Case: Best for businesses needing an easy-to-use, visual platform for building machine learning models without coding.

12. MLJAR

MLJAR is a cloud-based and open-source AutoML platform that automates model building and provides transparency into the process. It focuses on ease of use and explainability, making it a good choice for small and mid-sized businesses.

• Platform: Cloud-based and open-source
• Use Case: Suitable for small to mid-sized businesses needing affordable, easy-to-use AutoML with a focus on explainability.

For more AutoML

If you are interested, feel free to read our AutoML case studies article. AutoML is an important part of future of AI, for more on trends shaping AI, feel free to read our research on future of AI.

If you are unsure about where to start when choosing a vendor, we have data-driven lists of vendors for:

• ML software
• And AutoML software

External Links

• 1. McKinsey “Big data: The next frontier for innovation, competition, and productivity“
• 2. DataRobot
• 3. U.S. Bureau of Labor Statistics “Data Scientists“
• 4. Forbes “The Data Science Talent Gap: Why It Exists And What Businesses Can Do About It”
• 5. Wired “Building AI Is Hard—So Facebook Is Building AI That Builds AI”