List of Google Cloud Machine Learning Engine Customers
Google
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$425M
Since 2010, our global team of researchers has been studying Google Cloud Machine Learning Engine 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 Google Cloud Machine Learning Engine 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 Google Cloud Machine Learning Engine for ML and Data Science Platforms include: Rolls-Royce Holdings, a United Kingdom based Aerospace and Defense organisation with 42400 employees and revenues of $25.91 billion, NASA, a United States based Aerospace and Defense organisation with 18000 employees and revenues of $24.00 billion, HSBC, a United Kingdom based Banking and Financial Services organisation with 5836 employees and revenues of $4.95 billion, GrDF, a France based Oil, Gas and Chemicals organisation with 12000 employees and revenues of $2.00 billion, Monzo Bank, a United Kingdom based Banking and Financial Services organisation with 3700 employees and revenues of $1.57 billion and many others.
Contact us if you need a completed and verified list of companies using Google Cloud Machine Learning Engine, 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 Machine Learning software purchases.
The Google Cloud Machine Learning Engine customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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AirAsia | Transportation | 23000 | $838M | Malaysia | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2017 | n/a |
AirAsia implemented Google Cloud Machine Learning Engine in 2017 to introduce predictive models for ancillary service demand. The ML and Data Science Platforms deployment was scoped to sort and predict demand for ancillary services such as baggage, seats, and meals, aligning the application with commercial pricing and ancillary revenue management objectives.
Using Google Cloud Machine Learning Engine, AirAsia began with basic sorting capabilities trained on historical booking and transaction data to forecast demand patterns for individual ancillary products. In March 2018 the airline established the groundwork to expand from that basic sorting exercise to more advanced algorithmic models, signaling a staged approach to increasing model complexity and production readiness.
Operational coverage focused on ancillary revenue streams rather than core reservation processing, impacting pricing, revenue management, and merchandising functions. Governance and rollout were organized as iterative model releases, moving from simple demand-sorting workflows toward planned pricing and bundling experiments, with an explicitly stated end goal of having the system determine optimal customer pricing.
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Billie | Professional Services | 40 | $4M | Germany | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
In 2016, Billie implemented Google Cloud Machine Learning Engine on Google Cloud Platform to scale its model training and production scoring capabilities. Billie is a Germany based fintech with roughly 40 employees, and this implementation is part of its ML and Data Science Platforms strategy to support customer targeting and acquisition workflows.
The deployment used Google Cloud Machine Learning Engine to run managed model training jobs and to serve models for production inference, with pipelines designed around feature extraction and batch scoring. Google BigQuery served as the central data repository and feature store, feeding cleaned and aggregated datasets into training and prediction pipelines managed through the machine learning platform.
Operational scope included the data science and engineering teams, with model outputs integrated into commercial workflows to identify the best clients to target. Integrations explicitly include Google BigQuery and the Google Cloud Machine Learning Engine, and the overall architecture emphasized managed GCP services to reduce infrastructure operations.
Governance centered on centralizing data access in BigQuery and standardizing training and deployment pipelines under the data team, enabling operationalization of models into sales and marketing processes. The configuration allowed Billie to scale complex infrastructure quickly and efficiently using managed services on Google Cloud Platform, and to use Google BigQuery to identify the best clients to target.
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BOTfriends | Professional Services | 10 | $2M | Germany | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2017 | n/a |
In 2017, BOTfriends implemented Google Cloud Machine Learning Engine, an ML and Data Science Platforms solution, to support product development and model deployment. The team selected Google Cloud Machine Learning Engine on Google Cloud Platform citing the platform's easy to use interface and straightforward software development kits as key adoption factors. BOTfriends is a Germany based professional services startup of approximately 10 employees and focused the implementation on accelerating its conversational AI product development.
The implementation leveraged core ML and Data Science Platforms capabilities such as managed model training jobs and hosted prediction services provided by Google Cloud Machine Learning Engine. BOTfriends engineers used Google software development kits and Google's APIs to build iterative training workflows and to expose prediction endpoints for bot features, embedding model development into standard developer tooling and deployment processes.
Integration points emphasized platform native services, using Google Cloud Platform and Google's APIs as the operational backbone, and the company benefited from participation in GCP for Startups which supplied credits and developer hours to accelerate onboarding. Operational coverage was concentrated on the engineering and product teams responsible for bot development and for delivering ML driven features into client engagements across their services portfolio.
Governance focused on developer centric workflows and platform supported provisioning, driven by the support package provided through GCP for Startups. BOTfriends reported that Google's APIs outperformed alternatives tested and that the Google Cloud Machine Learning Engine delivered a more efficient way to build products, with credits and developer hours materially supporting initial adoption and developer productivity.
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Bulweria | Professional Services | 5 | $1M | United Kingdom | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
In 2016 Bulweria deployed Google Cloud Machine Learning Engine as the analytics backbone for its Car sharing Universal Platform, building on Google Cloud Platform to meet rapid scale requirements. The Car sharing Universal Platform had already been rolled out across more than 25 countries and ingested billions of data points per day from thousands of cars, with plans to extend to more than one million vehicles over the following year.
The implementation used Google Cloud Machine Learning Engine to provide core ML and model serving capabilities typical of ML and Data Science Platforms, including large scale model training, automated model serving for real time inference, and batch model scoring for historical analytics. Bulweria configured pipelines to support continuous training cycles and model versioning to adapt models to fast changing mobility patterns.
Architecturally the platform centralized telemetry ingestion from vehicle measurements, user data, and external transport systems worldwide and routed data into scalable cloud data pipelines and model endpoints. Operational coverage focused on fleet management, real time telemetry analytics, demand forecasting, and user behavior modeling, supporting mobility operations across multiple countries and sites.
Governance emphasized centralized model lifecycle management and staged rollouts across geographies to control model updates and inference quality, while operations teams were organized around analytics, engineering, and mobility product functions. Bulweria cited a significant cost and scalability advantage from running the solution on Google Cloud Platform, noting that cloud infrastructure was required to manage the anticipated vehicle scale, and reporting a large reduction in projected costs compared to older platforms.
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CareerBuilder | Professional Services | 3300 | $965M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
In 2016, CareerBuilder implemented Google Cloud Machine Learning Engine to enhance job search relevance on careerbuilder.com and across clients' talent networks. The deployment used Google Cloud Machine Learning Engine within the ML and Data Science Platforms category to operationalize models for relevance, matching, and ranking.
The implementation centralized model training and serving pipelines, employing the Google Cloud Machine Learning Engine for scalable model training, hyperparameter tuning, and prediction serving. Functional capabilities included feature extraction from job postings and candidate profiles, semantic matching models for job-candidate relevance, and both batch and online inference to support search and recommendation workflows. Configuration aligned with ML and Data Science Platforms practices, including model versioning and automated deployment workflows.
CareerBuilder integrated Google Cloud Talent Solution alongside Google Cloud Machine Learning Engine to combine talent-specific indexing with machine-learned relevance signals. Operational scope covered careerbuilder.com and clients' talent networks, and business functions impacted included consumer job search on the public site and talent acquisition tools delivered to client employers. Data pipelines ingested job listings and candidate interaction signals into Google Cloud for training and runtime scoring.
The vendor-provided Google machine learning capabilities were used to continuously refine search relevance, and CareerBuilder reported improved job search results on careerbuilder.com and across clients’ talent networks. This implementation exemplifies use of Google Cloud Machine Learning Engine within ML and Data Science Platforms to support production relevance and matching use cases for a commercial job board.
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Professional Services | 210 | $40M | Singapore | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 40 | $10M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 300 | $40M | Norway | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 13 | $2M | Sweden | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 1000 | $200M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Buyer Intent: Companies Evaluating Google Cloud Machine Learning Engine
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| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD Google Cloud Machine Learning Engine Coverage
Google Cloud Machine Learning Engine is a ML and Data Science Platforms solution from Google.
Companies worldwide use Google Cloud Machine Learning Engine, from small firms to large enterprises across 21+ industries.
Organizations such as Rolls-Royce Holdings, NASA, HSBC, GrDF and Monzo Bank are recorded users of
Google Cloud Machine Learning Engine for ML and Data Science Platforms.
Companies using Google Cloud Machine Learning Engine are most concentrated in Aerospace and Defense, Banking and Financial Services and Oil, Gas and Chemicals, with adoption spanning over 21 industries.
Companies using Google Cloud Machine Learning Engine are most concentrated in United Kingdom, United States and France, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Google Cloud Machine Learning Engine across Americas, EMEA, and APAC.
Companies using Google Cloud Machine Learning Engine range from small businesses with 0-100 employees - 41.03%, to mid-sized firms with 101-1,000 employees - 33.33%, large organizations with 1,001-10,000 employees - 12.82%, and global enterprises with 10,000+ employees - 12.82%.
Customers of Google Cloud Machine Learning Engine 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 Google Cloud Machine Learning Engine 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.