Automated Machine Learning Market Size, Growth Drivers, and Forecast (2025–2033) | Renub Research
Automated Machine Learning Market Size and Forecast (2025–2033)
The Automated Machine Learning (AutoML) Market is projected to grow from USD 2.70 billion in 2024 to USD 51.63 billion by 2033, expanding at a CAGR of 38.80% from 2025 to 2033.
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Key drivers include:
- Growing demand for AI democratization
- Shortage of skilled data scientists
- Need for faster model deployment
- Advancements in cloud computing
- Rising adoption across industries for scalable and efficient AI solutions
The market has been segmented by Offering, Enterprise Size, Deployment Mode, Application, End Use, and Geography.
Market Overview
Automated Machine Learning (AutoML) automates critical tasks in the machine learning workflow, including data preprocessing, feature selection, model selection, and hyperparameter tuning. This automation empowers even non-experts to rapidly build and deploy high-performing models, minimizing human error and reducing time-to-market.
With cloud innovations and AI democratization, AutoML is making machine learning more accessible, scalable, and cost-effective for industries such as healthcare, BFSI, retail, manufacturing, automotive, and telecommunications.
Key Growth Drivers
1. Increasing Data Volume and Complexity
Organizations are inundated with massive datasets from IoT sensors, business transactions, and social media. Traditional manual ML approaches are insufficient to handle such complexity. AutoML accelerates feature engineering, preprocessing, and model building, enabling faster and more accurate insights.
2. Advancements in Cloud Computing
Cloud platforms now offer scalable, on-demand computational power, eliminating the need for costly on-premise infrastructure. For instance, IBM upgraded its WatsonX AI platform in April 2024 with Meta Llama 3 integration, providing businesses with faster training, enhanced model libraries, and better deployment options.
3. AI Democratization
Collaborations like Google Cloud and NVIDIA’s expansion have enabled smaller firms to use powerful AI infrastructure without deep technical expertise. AutoML platforms lower the entry barrier, allowing businesses to adopt AI at scale.
Market Challenges
1. Data Privacy and Security
Industries handling sensitive information, such as healthcare and banking, face compliance challenges with GDPR and HIPAA. Cloud-based AutoML increases potential risks, making robust encryption and access controls critical.
2. Skills Gap in Model Interpretation
While AutoML builds models efficiently, interpreting results still requires statistical and domain expertise. Without proper understanding, organizations risk misinterpreting model outputs, leading to flawed decisions.
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Regional Insights
United States
The U.S. AutoML market benefits from strong AI adoption in healthcare, BFSI, and retail. Microsoft’s $19.7 billion acquisition of Nuance Communications boosted its AI capabilities, particularly in conversational AI for healthcare.
Germany
Germany’s AI adoption in manufacturing grew from 6% in 2020 to 13.3% in 2023, driven by Industry 4.0 initiatives. AutoML helps enterprises scale AI without large data science teams.
India
With government initiatives like the National Strategy for Artificial Intelligence, India is rapidly adopting AutoML in manufacturing, healthcare, and finance. The push for data-driven decision-making is boosting demand.
Saudi Arabia
Driven by Vision 2030, Saudi Arabia is integrating AutoML in oil & gas, banking, and healthcare. Investments in smart city projects and cloud infrastructure are fueling market expansion.
Market Segmentation
By Offering: Solutions, Services
By Enterprise Size: SMEs, Large Enterprises
By Deployment Mode: Cloud, On-Premise
By Application: Data Processing, Model Ensembling, Feature Engineering, Hyperparameter Optimization, Model Selection, Others
By End Use: Healthcare, Retail, IT & Telecom, BFSI, Automotive & Transportation, Advertising & Media, Manufacturing, Others
By Geography: North America, Europe, Asia-Pacific, Latin America, Middle East & Africa
Key Players
- DataRobot Inc.
- Amazon Web Services Inc.
- dotData Inc.
- IBM Corporation
- Dataiku
- SAS Institute Inc.
- Microsoft Corporation
- Google LLC (Alphabet Inc.)
- H2O.ai
- Aible Inc.
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