AutoML Tech / Products Comparison & Market Landscape [2023]

Automated machine learning (AutoML) is transforming the world of data science and AI. With AutoML, organizations can build highly accurate machine learning models with minimal human involvement. This enables data scientists, analysts, and developers to be more productive by automating repetitive tasks.

In this blog post, I will provide an in-depth comparison of the top AutoML technologies and products in 2024 based on my decade of experience in data analytics and machine learning. I have hands-on experience building models using many of the leading AutoML solutions discussed here.

My goal is to analyze their key capabilities, use cases, and the overall market landscape to help you choose the right AutoML platform for your needs.

A Quick Primer on AutoML

Before diving into the product comparison, let‘s quickly go over what AutoML is and why it‘s gained immense popularity:

• Automates ML Workflow: AutoML automates major parts of the machine learning workflow like data processing, feature engineering, model training, tuning and selection. This eliminates repetitive coding tasks that can take up to 80% of a data scientist‘s time.

• Democratizes AI: With AutoML, building ML models no longer requires advanced data science expertise. People with limited data skills can leverage AutoML to develop highly accurate models. AutoML solutions like DataRobot have easy to use visual interfaces.

• Saves Time & Resources: According to an MIT study, AutoML can help data scientists be 5-10x more productive by reducing development and deployment cycles from months to days or hours. This results in huge time and cost savings.

• Achieves Cutting-Edge Accuracy: Advanced AutoML solutions can automate complex tasks like neural architecture search to achieve state-of-the-art accuracy exceeding human performance. Some solutions like Darwin AutoML optimize architectures using evolutionary algorithms.

According to a Markets and Markets report, the global AutoML market size is projected to grow from USD 270 million in 2019 to USD 2,858 million by 2025, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. This exponential growth underscores the immense value AutoML is delivering across industries.

Evaluating the Top 7 AutoML Solutions

Now let‘s compare some of the leading AutoML technologies and commercial products in the market:

1. Google Cloud AutoML

• Overview: Serverless, fully-managed AutoML service on Google Cloud Platform (GCP)
• Key Strengths: Pre-trained models for vision, NLP, structured data; easy to use; integrated with GCP
• Limitations: Limited flexibility compared to open-source AutoML libraries
• Use Cases: Common ML tasks for mobile/web apps, chatbots, search optimization

Google Cloud AutoML provides pre-trained models through REST APIs for a variety of AI capabilities including image classification, object detection in images and video, text classification and sentiment analysis, and forecasting for structured data.

It has a simple graphical interface to upload your data, train models, and deploy them for prediction. The models run fully managed on Google‘s scalable cloud infrastructure.

Cloud AutoML is great for low-code development of AI directly through GCP services. However, you don‘t get the flexibility to customize algorithms and architectures like open-source AutoML libraries.

2. H2O Driverless AI

• Overview: Leading open-source AutoML platform focused on automatic feature engineering
• Key Strengths: Achieves state-of-the-art accuracy, model interpretability
• Limitations: Requires knowledge of Python; limited MLOps capabilities
• Use Cases: Risk modeling, predictive maintenance, campaign optimization

H2O Driverless AI automates key aspects of the machine learning workflow including feature engineering, model validation, tuning, ensembles and a proprietary feature importance technique.

It can achieve cutting-edge predictive accuracy with techniques like stacking ensemble models. Driverless AI emphasizes model transparency and interpretability.

However, it requires Python coding skills and has limited MLOps capabilities compared to some commercial AutoML solutions. The value lies in its open-source access to advanced AutoML algorithms.

3. DataRobot

• Overview: Leading commercial AutoML solution tailored for enterprises
• Key Strengths: Automates data prep and MLOps; easy visual interface
• Limitations: Limited flexibility compared to open-source platforms
• Use Cases: Banking, insurance, healthcare, retail industry applications

DataRobot enables enterprises to industrialize AI with high levels of automation. It automates data preparation, feature engineering, modeling, hyperparameter optimization, and deployment pipelines.

DataRobot‘s easy drag and drop web interface allows users to train and deploy models without coding. It also offers extensive MLOps capabilities like model monitoring, retraining triggers, and AI governance.

On the downside, DataRobot offers limited customization compared to open-source AutoML libraries. But it excels at enabling enterprise-wide scale and governance of AutoML.

4. Amazon SageMaker Autopilot

• Overview: Fully managed AutoML service for building, training and deploying models
• Key Strengths: Minimal coding; integration with AWS services
• Limitations: Limited flexibility beyond exploration of ML models
• Use Cases: Broad use cases including fraud detection and demand forecasting

Amazon SageMaker Autopilot provides full automation of data preprocessing, algorithm and hyperparameter selection, model training, evaluation, and deployment. It allows users to submit datasets and generate candidate ML models without coding.

The trained models can be directly deployed on SageMaker endpoints and integrated into downstream AWS services. However, Autopilot has limited flexibility for customization of AutoML techniques.

5. Darwin AutoML

• Overview: Automated neural architecture search to find optimal deep learning models
• Key Strengths: Specialized for state-of-the-art deep learning methods like computer vision
• Limitations: Limited documentation and commercial support
• Use Cases: Computer vision, advanced NLP models

Darwin AutoML utilizes evolutionary algorithms to automatically search for deep learning architectures optimized for a given dataset and task. It can optimize various neural architectures including CNNs, ResNets, LSTMs and transformers.

Darwin AutoML generates models that exceed human designed networks. However, it has limited documentation and commercial support compared to solutions like H2O and DataRobot.

6. AutoKeras

• Overview: Flexible open-source AutoML library for deep learning based on Keras
• Key Strengths: Broad capabilities for deep learning; easy integration
• Limitations: Limited automation of MLOps capabilities
• Use Cases: Image classification, text classification, structured data tasks

AutoKeras provides AutoML capabilities for deep neural networks using Keras as the underlying framework. It makes it easy to automatically conduct neural architecture search, model training, and hyperparameter tuning.

The benefit of AutoKeras is the modular and customizable architecture allowing integration of AutoML techniques into existing workflows. However, it does not address MLOps requirements like model monitoring, governance and retraining.

7. AutoGluon

• Overview: Open-source AutoML toolkit based on MXNet and Gluon
• Key Strengths: Automates text, image, and tabular data tasks
• Limitations: Limited customization compared to AutoKeras
• Use Cases: Broad deep learning and ML use cases

AutoGluon is an open-source AutoML toolkit based on the MXNet and Gluon deep learning libraries. It automates common data science and ML workflows including data preprocessing, training, hyperparameter tuning, stacking and ensembles.

The advantage of AutoGluon is the ease of use through its concise APIs. However, it offers less flexibility for customization compared to AutoKeras.

This comparison shows the diversity of AutoML solutions available today. The right product depends on your specific requirements and use cases. For example, Google Cloud AutoML and Amazon SageMaker Autopilot simplify cloud-based model building. H2O Driverless AI and DataRobot excel at complex enterprise applications. AutoKeras provides versatile deep learning capabilities.

Evolution of AutoML Technology

Modern AutoML solutions utilize a variety of advanced techniques:

• Neural Architecture Search (NAS): Automatically searches for optimal neural network architectures, outperforming human design. Used by Darwin AutoML.

• Transfer Learning: Leverages pre-trained models on adjacent tasks as a starting point to learn new tasks faster with smaller dataset sizes. Widely used technique.

• Reinforcement Learning: Trains models to sequentially make decisions and optimize complex objectives. Still an emerging technique.

• Ensemble Learning: Combines multiple models to improve overall predictive accuracy. Used by many solutions like H2O Driverless AI.

• Multi-Task Learning: Leverages layers trained on related tasks to better generalize to new ones. Used in Google Cloud AutoML.

We can expect more innovation in AutoML algorithms and architectures as research continues. AutoML solutions will progressively get better at automating complex modeling tasks across modalities and datasets.

When Does AutoML Deliver the Most Value?

Here are some of the most valuable use cases where AutoML solutions excel:

• Accelerated Model Development: AutoML speeds up development and deployment for data scientists by 5-10x as shown in studies. This enables faster iteration.

• Democratization for Business Users: Business users with limited data science expertise can leverage AutoML to build their own models without coding.

• Hyper-Personalization: AutoML rapidly trains high-performing models tailored to small segments, enabling hyper-personalization at scale.

• Anomaly Detection: Detect anomalies and outliers in real-time data streams by automating model builds with AutoML solutions like DataRobot.

• Predictive Maintenance: Forecast equipment failure and optimize maintenance schedules by leveraging AutoML for time series forecasting.

• Customer Churn Prediction: Identify customers at high risk of churning and take proactive retention measures using automated modeling.

• Fraud Detection: Detect fraudulent transactions, claims, accounts in real-time by rapidly training and deploying models.

Current & Future AutoML Market Size

The rapid growth of AutoML underscores its immense value across industries:

• According to the Markets and Markets report cited earlier, the global AutoML market size is projected to grow from USD 270 million in 2019 to USD 2,858 million by 2025, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period.

• North America accounted for the largest market share of ~35% in 2019. Fast adoption by tech giants like Google, Amazon, and startups like H2O.ai and DataRobot drive growth.

• The banking, financial services, insurance, automotive, healthcare, retail and manufacturing industries are expected to aggressively adopt AutoML.

• With exponentially growing data, competitive necessity will force more organizations to leverage AutoML.

Projected Global AutoML Market Size

Year	Market Size (USD million)	Growth Rate
2019	270	–
2020	525	94.4%
2021	850	61.9%
2022	1269	49.3%
2023	1758	38.5%
2024	2301	30.8%
2025	2858	24.2%

Source: Markets and Markets AutoML Report 2020

Key growth drivers fueling the AutoML market include:

• Surging demand for better customer experiences and hyper-personalization.

• Need to shorten development cycles and operationalize ML rapidly.

• Imperative to democratize AI and simplify model building for non-experts.

• Initiatives to leverage open-source libraries and cloud-based platforms.

Key Takeaways on the AutoML Landscape

Based on our analysis, here are my key conclusions regarding the AutoML ecosystem:

• Simplifies ML Workflows: AutoML solutions like H2O Driverless AI can automate >80% of repetitive ML tasks helping data scientists focus on high-value work.

• Allows Faster Experimentation: AutoML enables rapid iteration and testing of modeling ideas, boosting productivity 5-10x.

• Achieves Cutting-Edge Accuracy: Advanced techniques like neural architecture search push accuracy higher, sometimes exceeding human performance.

• Democratizes AI: AutoML lowers barriers allowing business users with limited data expertise to train their own models.

• MLOps Integration: Commercial solutions like DataRobot allow easy model monitoring, governance and retraining improving operationalization.

• Market Growth Accelerating: AutoML adoption is surging fueled by need for faster development cycles, easy-to-use solutions and growing data.

The bottom line is that AutoML is transforming ML workflows. It is enabling organizations to operationalize AI and empowering business users to leverage data. With compute and data growing exponentially, expect AutoML to become table stakes capability for enterprises.