Machine Learning Operations (MLOps) Statistics By Market Growth And Industries (2026)

Briefing

Machine Learning Operations (MLOps) Statistics: Machine Learning Operations (MLOps) makes it easier for businesses to put machine learning models into action. Building a model is only the first step. Companies also need to deploy it, monitor its performance, fix issues, and keep it up to date as data changes.

MLOps brings these tasks together in one organized process. It connects data science, development, and operations teams, helping them work more efficiently. With the right MLOps practices, businesses can launch models faster, reduce errors, and maintain reliable results. As AI becomes a bigger part of everyday business, MLOps is becoming essential for managing machine learning at scale.

This article explores its benefits, challenges, and key trends.

Featured Choice

1. The global MLOps market is expected to increase from USD 4.27 billion in 2025 to USD 6.11 billion in 2026.
2. Based on component, platforms accounted for the largest share of the MLOps market, with 64.23% of revenue in 2025.
3. The BFSI segment is expected to generate USD 2,985.6 million by 2030, growing at a 38.8% CAGR from 2025 to 2030.
4. North American region is projected to reach USD 6,236.1 million by 2030, growing at a 37.8% CAGR from 2025 to 2030.
5. Software and technology leads with 13.7% of companies running 100 or more ML models in production.
6. Microsoft Cloud revenue reached USD 214.4 billion in 2026, up from USD 168.9 billion in 2025 and USD 137.7 billion in 2024.
7. 48% of businesses use machine learning, deep learning, or NLP to manage large data sets more effectively.
8. More than 80% of enterprises are expected to adopt generative AI models by 2026.
9. In 2025, AI adoption reached 87% among large enterprises, driving demand for MLOps. 
10. MLOps job postings grew 9.8 times over five years.
11. An intermediate certification costs about USD 300 and is best for AWS-based MLOps and machine learning workflows.

MLOps Market Growth Outlook

(Source: market.us)

• The global MLOps market is expected to increase from USD 4.27 billion in 2025 to USD 6.11 billion in 2026.
• By 2033, the market is forecast to reach USD 75.42 billion, growing at a strong CAGR of 43.2% from 2023 to 2033.

Key Segment Insights in 2025-2031

(Reference: mordorintelligence.com)

• Based on component, platforms accounted for the largest share of the MLOps market, with 64.23% of revenue in 2025. Meanwhile, services are expected to grow at a 41.34% CAGR through 2031.
• By deployment mode, cloud solutions generated 53.44% of MLOps market revenue in 2025. Cloud deployment is also projected to record the fastest growth, with a 40.87% CAGR through 2031.
• By organization size, large enterprises held 54.90% of revenue in 2025. However, SMEs are expected to expand faster, registering a 41.76% CAGR through 2031.
• By end-user industry, BFSI led with 22.11% of revenue in 2025, while healthcare and life sciences are projected to grow at a 40.43% CAGR through 2031.
• By geography, North America led with 34.22% of revenue in 2025. Asia-Pacific is expected to be the fastest-growing region, growing at a 41.63% CAGR from 2026 to 2031, according to a report by Mordor Intelligence.

Global MLOps Market by Industry Vertical

Adoption Across Industries

(Source: tredence.com)

• Software and technology leads with 13.7% of companies running 100 or more ML models in production, followed by consumer packaged goods at 8.2%.
• Among consumer packaged goods firms, 22% run 1-10 models, 39% run 11-50, 26% run 51-100, 8% run over 100, and 5% are unsure.
• Financial services show 23% at 1-10 models, 42% at 11-50, 24% at 51-100, and 7% above 100, while health care reports 23%, 38%, 27%, and 6% respectively.
• Software and technology have the strongest top tier, with 14% above 100 models, against an overall average of 9%.

Machine Learning Operations Market by Regional Forecast, 2030

• North America: Grand View Research projects the region to reach USD 6,236.1 million by 2030, growing at a 37.8% CAGR from 2025 to 2030.
• Europe: The market is expected to reach USD 4,219.8 million by 2030, with a 39.3% CAGR.
• Asia Pacific: Revenue is forecast to reach USD 4,784.7 million by 2030, with a CAGR of 45.2%.
• Middle East & Africa: The market is projected to reach USD 681.2 million, expanding at a 44.3% CAGR.
• Latin America: Revenue will reach USD 691.7 million by 2030, growing at a 41.6% CAGR.

By Country Analysis

MLOps Company Statistics

• Microsoft: Microsoft Cloud revenue reached USD 214.4 billion in 2026, up from USD 168.9 billion in 2025 and USD 137.7 billion in 2024. Azure generated USD 101.9 billion, while Microsoft’s market cap was about USD 3.65 trillion.
• AWS: Q2 2026 sales reached USD 42.2 billion, up 37%, with a USD 169 billion annualized run rate. Amazon’s market cap was about USD 2.68 trillion.
• Google Cloud: In Q2 2026, revenue increased 82% to USD 24.8 billion, with a USD 514 billion backlog. Alphabet’s revenue reached USD 119.8 billion, while its market cap was USD 4.228 trillion.
• Databricks: Revenue run rate exceeded USD 7 billion, growing more than 80%. Moreover, 1,000+ customers are spending over USD 1 million. Its valuation reached USD 190 billion.
• IBM: In Q2 2026, revenue was USD 17.16 billion, up 1%, while software revenue reached USD 7.8 billion, up 5%. Market cap accounted for USD 231.44 billion.
• Snowflake: In 2026, product revenue reached USD 4.47 billion, up 29%, with 733 customers exceeding USD 1 million in revenue. Market cap was USD 117.14 billion.
• DataRobot: It had 850 customers and 869 employees and generated USD 285 million in revenue in 2024. Its latest valuation was USD 6.3 billion.
• Dataiku: ARR exceeded USD 350 million, with about 750 customers and 1,300 employees. Its valuation was USD 3.7 billion.
• H2O.ai: The company had 330 employees, an estimated revenue of USD 75 million, and more than 18,000 companies using its technology. Its valuation was USD 1.7 billion.
• Domino Data Lab: Estimated annual revenue was USD 22.6 million, with 266 employees. The adoption by more than 20% of Fortune 100 companies and its private valuation was about USD 915.87 million.

Key Machine Learning Statistics

• Market.us Scoop reported that 48% of businesses use machine learning, deep learning, or NLP to manage large data sets more effectively.
• 25% of IT specialists want to use machine learning for security
• Meanwhile, 16% see strong value in marketing and sales.
• 80% of respondents reported that AI is helping increase revenue.
• The AI hardware market was projected to reach USD 87.68 billion by 2026, growing at a 37.60% CAGR from 2019 to 2026.

MLOps and AI Investment Trends

• More than 80% of enterprises are expected to adopt generative AI models by 2026, according to Straits Research.
• Machine Learning attracted the largest share of AI investment at 62%, followed by Computer Vision at 31%.
• Autonomous Vehicles accounted for 4% of investment, while Smart Robotics and Virtual Agents each received 2%.

Production ML Statistics

(Source: Ethical Institute)

• The 2025 survey collected responses from 135 ML practitioners.
• 42% identified monitoring model performance as their top production challenge.
• 35% mainly used PyTorch, Lightning, or Fast.ai, putting these tools ahead of scikit-learn.
• AI risk and governance teams grew by 21% year over year.

Enterprise MLOps Adoption and Market Growth

• In 2025, AI adoption reached 87% among large enterprises, driving demand for MLOps. 
• Managing 5+ tools often makes MLOps integration and authentication more challenging.
• Edge AI could reach USD 66B by 2030, growing at a 21% CAGR and driving demand for hybrid MLOps.
• The global MLOps market is projected to exceed USD 39 billion by 2034.

MLOps Workforce and Skills Trends

• MLOps job postings grew 9.8 times over five years, according to arcade.dev.
• 77% of AI-related job postings require machine learning skills.
• Meanwhile, 57% seek versatile professionals.
• Data scientist pay rose by 30% to USD 152,000 in 2025, up from USD 117,000 in 2024.
• 72% of IT leaders report an AI skills gap.

MLOps Infrastructure and Tooling Trends

• Only 54% of AI models move from pilot to production.
• 64.3% of large enterprises use on-premises systems for sensitive workloads.
• Organizations evaluate an average of 10 AI use cases.
• 72% of decision-makers plan to expand their use of GenAI.
• GPUs account for 60% of ML spending, while 47% of projects face budget constraints.
• Edge AI could reach USD 56.8 billion by 2030, growing at a 36.9% CAGR.

MLOps Engineer Learning Roadmap for 2026

MLOps Certifications and Training Programs

• AWS Certified Machine Learning-Specialty: According to Scaler, an intermediate certification costing about USD 300, best for AWS-based MLOps and machine learning workflows.
• Google Cloud Professional ML Engineer: An intermediate certification priced at around USD 200, suitable for GCP, AutoML, and Vertex AI.
• Azure AI Engineer Associate: This intermediate certification costs about USD 165 and focuses on Azure AI and machine learning services.
• Certified Kubernetes Administrator: At approximately USD 395, this intermediate certification is useful for Kubernetes-based model serving.
• TensorFlow Developer Certificate: A beginner-level option costing about USD 100 for deep learning development.
• MLflow Certified Developer: An intermediate certification with variable pricing, focused on experiment tracking and model management.
• Databricks Certified ML Practitioner: This intermediate certification costs about USD 200 and covers MLflow, Spark ML, and Delta Lake.

Summary

MLOps makes it easier for businesses to use machine learning in the real world. It helps teams build, deploy, monitor, and improve models without adding unnecessary complexity. As AI becomes a bigger part of business operations, the need for reliable MLOps will continue to grow.

By using the right MLOps practices, companies can save time, reduce errors, keep models running smoothly, and get more value from their AI investments.

FAQ