List of Google Cloud Machine Learning Engine Customers
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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, Smart Parking, a Australia based Professional Services organisation with 400 employees and revenues of $2.20 billion, GrDF, a France based Oil, Gas and Chemicals organisation with 12000 employees and revenues of $2.00 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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Professional Services | 5 | $1M | United Kingdom | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 3300 | $965M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 200 | $15M | 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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