Machine learning software
data
Identify companies using TensorFlow, AWS SageMaker, Azure Machine Learning, Hugging Face, MLflow, H2O.ai and other ML platforms for AI model development, MLOps, training and deployment workflows.
- Verified ML platform contacts
- ML engineers email list B2B
- MLOps platform database
- CSV or Excel CRM-ready delivery
Request Your ML Users List
Get a free sample of verified machine learning software contacts for AI software sales, MLOps campaigns, ABM and market research.
What is Machine learning software data?
Machine learning software data helps B2B teams identify companies using ML platforms such as TensorFlow, AWS SageMaker, Azure Machine Learning, Hugging Face, MLflow and H2O.ai. It provides platform-level context on AI model development, data science workflows, MLOps, model deployment, AI governance and cloud AI infrastructure, making it useful for AI software vendors, MLOps providers, cloud consultants, data engineering firms and ABM teams targeting ML-led organisations.
- Machine learning software users data
- Companies using TensorFlow & SageMaker
- Azure ML & Hugging Face user contacts
- MLOps platform usage insights
- AI model development company data
- ML engineers & data science contacts
- Cloud AI infrastructure targeting
- CRM-ready data for ABM campaigns
- Machine learning software data
- Companies using TensorFlow & SageMaker
- Azure ML & Hugging Face user contacts
- MLOps platform usage insights
- AI model development company data
- ML engineers & data science contacts
- Cloud AI infrastructure targeting
- CRM-ready data for ABM campaigns
Every MLOps record. Fully enriched.
Each MLOps platform database record is structured for sales, marketing, CRM, ABM, AI consulting outreach and market research workflows.
Contact Name
ML Job Title & Seniority
Verified Business Email
Phone
Company Name & Website
LinkedIn Profile URL
Industry & Business Sector
Revenue & Employee Size
City, State, Country & Region
ML Platform In Use
ML Category & MLOps Workflow
Built for AI growth teams
This machine learning software data helps teams target companies by ML platform, MLOps workflow, cloud provider, AI maturity, industry, geography and buyer role.
- AI software vendors targeting ML platform users
- MLOps and model monitoring providers
- Cloud consultants targeting SageMaker and Azure ML users
- Data engineering firms supporting ML pipelines
- AI governance, model risk and security vendors
- Developer tool and DevOps vendors for ML teams
- ABM teams segmenting by ML platform and cloud provider
- Demand generation teams targeting ML-led organisations
15+ machine learning platforms. One database.
Filter by machine learning platform, AI development tool, region or company size and pull a clean list in minutes.
| Machine Learning Platform | Verified Contacts | Companies | Coverage | Action |
|---|---|---|---|---|
GVGoogle Vertex AIML #01 | 11.3k | 2.3k | Global | Get sample → |
ASAmazon SageMakerML #02 | 43.7k | 8.7k | Global | Get sample → |
AMMicrosoft Azure Machine LearningML #03 | 30k | 6k | Global | Get sample → |
DBDatabricks ML + LakehouseML #04 | 97.4k | 19.5k | Global | Get sample → |
IWIBM Watsonx.aiML #05 | 1.6k | 311 | Global | Get sample → |
SVSAS ViyaML #06 | 5.4k | 1.1k | Global | Get sample → |
DRDataRobotML #07 | 3k | 592 | Global | Get sample → |
DIDataikuML #08 | 6k | 1.2k | Global | Get sample → |
HOH2O.aiML #09 | 2.5k | 509 | Global | Get sample → |
ALAlteryx Machine LearningML #10 | 1.3k | 265 | Global | Get sample → |
ARAltair RapidMinerML #11 | 1.6k | 329 | Global | Get sample → |
KNKNIMEML #12 | 7.7k | 1.5k | Global | Get sample → |
DLDomino Data LabML #13 | 535 | 107 | Global | Get sample → |
AEAnaconda EnterpriseML #14 | 3.6k | 728 | Global | Get sample → |
MAMATLAB MathWorksML #15 | 97.5k | 19.5k | Global | Get sample → |
Machine learning data FAQs
Common questions about machine learning software data, ML platform targeting and MLOps contact lists.
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machine learning data
Review verified machine learning platform contacts before purchasing a full dataset. Your free sample may include company details, platform usage, ML category, job title, business email, phone number where available, industry, location, employee size and CRM-ready segmentation fields.
Best Fit Use Cases for Machine Learning Software Data
A practical reference for B2B teams running machine learning software lead generation, MLOps campaigns, AI model development targeting, ABM outreach, partner recruitment and market research.
Where machine learning software data fits best
Machine learning software data is best suited for B2B teams that need platform-specific targeting across ML frameworks, cloud ML platforms, MLOps tools, model training environments and AI model development workflows. It helps sales and marketing teams identify companies using TensorFlow, AWS SageMaker, Azure Machine Learning, Hugging Face, MLflow, H2O.ai and related machine learning platforms.
The dataset is especially useful for TensorFlow users contact database targeting, companies using AWS SageMaker outreach, Azure Machine Learning users list campaigns, Hugging Face customers database targeting, MLflow users contact data outreach, H2O.ai customers list campaigns and ML engineers email list B2B targeting.
It also supports MLOps platform database enrichment, companies building AI models contact list outreach, verified machine learning platform contacts targeting, AI governance and model risk campaigns, cloud AI and data engineering campaigns, MLOps and model monitoring campaigns, partner recruitment, market research and territory planning.
How to use the dataset for campaigns
Start by selecting the machine learning platform you want to target, such as TensorFlow, AWS SageMaker, Azure Machine Learning, Hugging Face, MLflow, H2O.ai, Kubeflow, Databricks Mosaic AI or related ML platforms. Then choose the workflow that matches your offer, including model training, model deployment, MLOps, experiment tracking, model registry, AutoML, model monitoring, generative AI, NLP, computer vision or production AI workflows.
Next, define the buyer roles most relevant to your campaign, such as ML Engineer, Machine Learning Engineer, Data Scientist, MLOps Engineer, AI Engineer, Chief Data Officer, CTO, VP Engineering, AI Product Manager or Head of Data Science. Apply company filters by industry, employee size, revenue range, geography, cloud provider and company type to improve campaign accuracy.
Once the dataset is segmented, use it for cold email, calling, LinkedIn outreach, ABM campaigns, partner recruitment, CRM enrichment, machine learning campaigns or market research. If your offer depends on knowing which machine learning platform a company uses, this data gives your team a more relevant starting point before outreach.
Select ML platforms and workflows
Start by choosing the machine learning platforms you want to target, such as TensorFlow, AWS SageMaker, Azure Machine Learning, Hugging Face, MLflow, H2O.ai, Kubeflow or Databricks Mosaic AI. Then filter by model training, deployment, MLOps, AutoML, model monitoring, generative AI, NLP or computer vision workflows.
Define buyer roles and filters
Build your campaign around the right decision-makers, including ML Engineers, Data Scientists, MLOps Engineers, AI Engineers, Chief Data Officers, CTOs, VP Engineering, AI Product Managers and Heads of Data Science. Improve targeting accuracy with filters for industry, employee size, revenue range, geography, cloud provider and company type.
Launch targeted AI outreach
Use the segmented machine learning software data for cold email, calling, LinkedIn outreach, ABM campaigns, partner recruitment, CRM enrichment, machine learning campaigns and market research. When your offer depends on knowing which ML platform a company uses, this data gives your team a stronger starting point before outreach.
Why machine learning platform data matters
Machine learning buyers are not general technology contacts. They are usually linked to data science, AI product development, model training, deployment pipelines, cloud infrastructure, model governance, experiment tracking, data quality and MLOps maturity.
Companies using TensorFlow may be focused on deep learning, model development, computer vision, natural language processing or production ML workloads, making the outreach context very different from a generic IT campaign.
AWS SageMaker users may be building, training or deploying ML models inside AWS environments, while Azure Machine Learning users may be managing enterprise ML workflows, automated ML, governance and Microsoft-connected data systems.
Hugging Face users may work with open-source models, NLP, generative AI and transformer workflows. MLflow users may focus on experiment tracking, model registry and lifecycle management, while H2O.ai users may focus on AutoML and predictive modelling.
When your campaign knows the machine learning platform a company uses, you can align your message with its ML stack, cloud environment, model development process, AI maturity, governance needs, data pipeline gaps or MLOps roadmap.
