Machine Learning Database

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
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313k+ Verified Contacts
95% Accuracy Rate
60+ Countries
45 days Refresh Cycle
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Machine learning software users data
Overview

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
Data Includes

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

Who’s this for?

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
Machine Learning Data Covered

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 PlatformVerified ContactsCompaniesCoverageAction
Google Vertex AIML #01
11.3k2.3kGlobalGet sample →
Amazon SageMakerML #02
43.7k8.7kGlobalGet sample →
Microsoft Azure Machine LearningML #03
30k6kGlobalGet sample →
Databricks ML + LakehouseML #04
97.4k19.5kGlobalGet sample →
IBM Watsonx.aiML #05
1.6k311GlobalGet sample →
SAS ViyaML #06
5.4k1.1kGlobalGet sample →
DataRobotML #07
3k592GlobalGet sample →
DataikuML #08
6k1.2kGlobalGet sample →
H2O.aiML #09
2.5k509GlobalGet sample →
Alteryx Machine LearningML #10
1.3k265GlobalGet sample →
Altair RapidMinerML #11
1.6k329GlobalGet sample →
KNIMEML #12
7.7k1.5kGlobalGet sample →
Domino Data LabML #13
535107GlobalGet sample →
Anaconda EnterpriseML #14
3.6k728GlobalGet sample →
MATLAB MathWorksML #15
97.5k19.5kGlobalGet sample →
Machine learning data includes Google Vertex AI, Amazon SageMaker, Microsoft Azure Machine Learning, Databricks, IBM Watsonx.ai, SAS Viya, DataRobot, Dataiku, H2O.ai, KNIME, Anaconda Enterprise and more.
FAQs

Machine learning data FAQs

Common questions about machine learning software data, ML platform targeting and MLOps contact lists.

Talk to an expert
Machine learning software data is a verified B2B dataset of companies using ML frameworks, cloud ML platforms, MLOps tools, model training environments, model deployment systems and machine learning infrastructure. It includes company details, decision-maker contacts, platform usage, industry, location and CRM-ready fields for sales, marketing, ABM and AI consulting outreach.
Yes. DiscoverMSPs can provide a TensorFlow users contact database based on your target industry, geography, company size, job title and campaign objective. This data is useful for MLOps, AI infrastructure, model deployment, data engineering, model monitoring and AI governance campaigns.
Yes. Companies using AWS SageMaker can be targeted for cloud AI consulting, MLOps implementation, model monitoring, AWS security, data pipeline support, feature engineering, AI governance and model deployment campaigns across relevant industries and regions.
Yes. Azure Machine Learning users list data helps Microsoft partners, AI consultants and cloud data firms reach companies using Azure ML for model training, automated ML, deployment, governance and enterprise AI workflows.
Yes. Hugging Face customers database segments help AI vendors and consultants target companies using Hugging Face for transformer models, NLP, generative AI, model hosting and AI application development.
Yes. MLflow users contact data helps vendors reach companies using MLflow for experiment tracking, model registry, model versioning, deployment workflows and machine learning lifecycle management.
Explore

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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.

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The Complete Guide

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

ML Buyers

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.

TensorFlow

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.

Cloud ML

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.

AI Stack

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.

Outreach Fit

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.

Questions buyers ask us most

Yes. H2O.ai customers list data helps vendors target companies using H2O.ai for automated machine learning, predictive modelling, model training and enterprise AI workflows.
An ML engineers email list B2B includes technical contacts responsible for machine learning models, MLOps, data science infrastructure, model deployment, model monitoring, AI pipelines and production ML workflows.
An MLOps platform database includes companies using software to manage machine learning operations, including experiment tracking, model registry, model deployment, monitoring, governance, reproducibility and lifecycle management.
Yes. Companies building AI models contact list data helps vendors reach organisations actively developing, training, deploying or managing machine learning and AI models.
Verified machine learning platform contacts are business contacts associated with companies using ML platforms. They may include ML Engineers, Data Scientists, MLOps Engineers, AI Engineers, CTOs, Chief Data Officers and VP Engineering leaders.
The data is usually delivered in CSV or Excel format and can be prepared for CRM, email outreach, sales engagement, ABM platforms or marketing automation systems.

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