List of HeavyML Customers
HEAVY.AI
100 Montgomery St, Fifth floor,
San Francisco, 94104, CA,
United States
San Francisco, 94104, CA,
United States
1 415-851-4787
Professional Services
105
$21M
Since 2010, our global team of researchers has been studying HeavyML customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased HeavyML for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using HeavyML for ML and Data Science Platforms include: Verizon, a United States based Communications organisation with 99400 employees and revenues of $134.79 billion, S&P Global, a United States based Banking and Financial Services organisation with 42350 employees and revenues of $14.21 billion, Entel, a Chile based Communications organisation with 12132 employees and revenues of $2.87 billion and many others.
Contact us if you need a completed and verified list of companies using HeavyML, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Entel | Communications | 12132 | $2.9B | Chile | HEAVY.AI (formerly OmniSci) | HeavyML | ML and Data Science Platforms | 2023 | n/a |
In 2023 Entel deployed HeavyML from HEAVY.AI as part of its ML and Data Science Platforms footprint to accelerate telecom network analytics. The deployment emphasizes interactive mapping and analysis of massive mobile and network datasets to detect performance issues and improve customer experience in Chile.
HeavyML's in database predictive modeling capability is used alongside HEAVY.AI interactive analytics to support geospatial visualization and high throughput time series analysis of network telemetry. The implementation concentrates on keeping compute close to data for exploratory analytics and model inference, aligning with ML and Data Science Platforms patterns for large scale telemetry processing.
Operational coverage centers on Entel network operations and customer experience teams in Chile, who use the platform to interrogate large mobile and network datasets for performance triage and operational troubleshooting. Integrations are focused on ingesting and querying massive mobile and network data sources, supporting iterative analytics workflows and operational dashboards.
Given HEAVY.AI's 2023 HeavyML release of in database predictive modeling, it is reasonable to infer Entel could apply HeavyML for network anomaly detection and capacity forecasting, though use of those specific predictive workflows is inferred rather than explicitly named in Entel's published case study.
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S&P Global | Banking and Financial Services | 42350 | $14.2B | United States | HEAVY.AI (formerly OmniSci) | HeavyML | ML and Data Science Platforms | 2023 | n/a |
In 2023 HEAVY.AI added HeavyML as an in-database predictive modeling capability, and S&P Global's IHS Markit uses HEAVY.AI to power its Energy Studio Impact interactive energy analytics. S&P Global implemented HEAVY.AI to accelerate map and chart rendering and to shorten time-to-insight for energy production, reserves, economics, and finance analyses. HeavyML sits in the ML and Data Science Platforms category and aligns with S&P Global's analytics footprint for model driven scenario work.
Energy Studio Impact implements interactive visualization and GPU accelerated geospatial rendering that handles large scale energy datasets, delivering dramatically faster map and chart rendering and much quicker time-to-insight as described in the case study. HeavyML provides in-database model training and inference capabilities that can be embedded alongside those visual analytics workflows, enabling predictive energy scenario modeling and forecasting. While the published case study does not explicitly call out HeavyML, it is a reasonable inference that S&P Global could adopt HeavyML to extend Energy Studio with integrated predictive modeling.
Operational coverage described in the deployment centers on energy production, reserves, economics, and finance analyses, indicating cross functional use by analytics and product teams within S&P Global's energy research organization. The documented implementation ties the HEAVY.AI execution engine to Energy Studio, and HeavyML would reduce data movement by executing models adjacent to visualization and analytics layers. This configuration supports workflows where in-database prediction and interactive exploration are combined for scenario analysis.
Adoption of HeavyML in this context would imply introducing model governance, versioning, and in-database execution workflows into existing analytics pipelines, consistent with practices common to ML and Data Science Platforms. Rollout would require coordination between analytics engineers and domain experts to integrate predictive models into the Energy Studio driven reporting and forecasting workflows. The case study explicitly documents rendering and time-to-insight benefits for HEAVY.AI driven Energy Studio, and HeavyML represents a logical platform extension to add built in predictive capabilities.
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Verizon | Communications | 99400 | $134.8B | United States | HEAVY.AI (formerly OmniSci) | HeavyML | ML and Data Science Platforms | 2023 | n/a |
In 2023 HEAVY.AI introduced HeavyML and Verizon has access to and is evaluating HeavyML as part of its use of HEAVY.AI's GPU accelerated analytics to analyze billions of streaming records per week for network reliability, investment optimization, and near real time monitoring in the United States. Verizon leverages HEAVY.AI's GPU accelerated analytics at scale, and the 2023 HeavyML release extends that analytics stack with in database machine learning capabilities relevant to its streaming telemetry workloads.
HeavyML is positioned as an in database ML capability within the ML and Data Science Platforms category, enabling model training and inference close to GPU resident data and query processing. The application supports workflows common to ML and Data Science Platforms, including model training, model scoring, and feature preparation executed inside the analytic engine to reduce data movement and accelerate iteration on predictive models.
Functional emphasis for Verizon is inferred from the public case description, focusing on forecasting and anomaly detection applied to billions of streaming records per week, near real time scoring across streams, and analytics to support investment optimization and reliability monitoring. The architecture therefore centers on a GPU accelerated analytics engine paired with in database ML to enable persistent model artifacts, repeated inference, and near real time analytical queries over high velocity data.
Operational coverage is United States network telemetry and investment planning environments, with evaluation activity concentrated on integrating predictive modeling into network reliability and monitoring workflows. Verizon is described as evaluating these HeavyML predictive modeling capabilities for forecasting and anomaly detection, which implies phased testing and governance by analytics and operations teams before broader operationalization.
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FAQ - APPS RUN THE WORLD HeavyML Coverage
HeavyML is a ML and Data Science Platforms solution from HEAVY.AI (formerly OmniSci).
Companies worldwide use HeavyML, from small firms to large enterprises across 21+ industries.
Organizations such as Verizon, S&P Global and Entel are recorded users of
HeavyML for ML and Data Science Platforms.
Companies using HeavyML are most concentrated in Communications and Banking and Financial Services, with adoption spanning over 21 industries.
Companies using HeavyML are most concentrated in United States and Chile, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of HeavyML across Americas, EMEA, and APAC.
Companies using HeavyML range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 0%, and global enterprises with 10,000+ employees - 100%.
Customers of HeavyML include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified HeavyML customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.