List of Optuna Customers
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Since 2010, our global team of researchers has been studying Optuna 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 Optuna for AI Frameworks and Libraries 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 Optuna for AI Frameworks and Libraries include: Canva, a Australia based Professional Services organisation with 5500 employees and revenues of $1.50 billion, Hugging Face, a United States based Professional Services organisation with 500 employees and revenues of $50.0 million, Valohai, a United States based Professional Services organisation with 35 employees and revenues of $4.0 million and many others.
Contact us if you need a completed and verified list of companies using Optuna, 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.
The Optuna 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 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 | Insight Source |
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Canva | Professional Services | 5500 | $1.5B | Australia | Optuna | Optuna | AI Frameworks and Libraries | 2021 | n/a | In 2021, Canva implemented Optuna as the optimization engine within its Kubernetes and Argo based distributed hyperparameter tuning workflow. Optuna was integrated into Canva's AI Frameworks and Libraries stack to support model tuning for recommendations, information retrieval and natural language processing in the Australia region. The implementation positions Optuna as the central sampler and trial orchestrator, with Argo workflows scheduling parallel trials across Kubernetes cluster nodes. The distributed hyperparameter tuning workflow targets ML model training and experimentation and accelerates iteration on production models. Canva documents the Optuna integration in an engineering blog post. Operational coverage included ML engineering teams responsible for recommendations, retrieval and NLP, with experiments executed on Kubernetes clusters in Australia. Governance relied on Argo to standardize experiment execution, monitoring and reproducibility, embedding Optuna driven trials into the orchestration layer. Canva reported that optimization time fell from over a week to a little over a day. | |
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Hugging Face | Professional Services | 500 | $50M | United States | Optuna | Optuna | AI Frameworks and Libraries | 2020 | n/a | In 2020, Hugging Face implemented Optuna within its Transformers training stack. The implementation integrates Optuna as a first class hyperparameter_search backend in the Transformers Trainer, positioning Optuna within the AI Frameworks and Libraries category for model training experimentation. The integration enables Optuna-based hyperparameter optimization workflows for NLP and other model training tasks, including multi-objective optimization and distributed training scenarios using PyTorch DistributedDataParallel. The Transformers Trainer invokes Optuna via the hyperparameter_search API to orchestrate trial execution, objective evaluation, and early stopping within training loops. This integration is documented in the Transformers official documentation and Optuna support is explicitly listed in the Hugging Face hyperparameter search documentation, supporting practitioners in the United States and worldwide. Operationally the integration is applied in the ML/model training experimentation process area, where teams embed Optuna into experimentation pipelines and model training workflows. | |
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Valohai | Professional Services | 35 | $4M | United States | Optuna | Optuna | AI Frameworks and Libraries | 2020 | n/a | In 2020 Valohai integrated Optuna as a supported and default Bayesian optimization engine within its MLOps platform, documented in Valohai's public docs. This implementation is categorized under AI Frameworks and Libraries and is directly associated with the model training and experimentation process area. Optuna is embedded in Valohai Tasks and experiment orchestration to provide automated hyperparameter tuning and Bayesian optimization workflows. The implementation exposes Optuna driven Bayesian optimization as a configured module used to orchestrate and parallelize hyperparameter optimization jobs, aligning with standard HPO workflows and experiment lifecycle management. The deployment is delivered as part of Valohai's platform offering and is available to customers in Finland and globally, enabling teams to run Optuna driven Bayesian optimization at scale through Valohai Tasks orchestration. Module usage and configuration details are explicit in Valohai documentation, which guides operational usage, experiment governance, and the orchestration of parallel HPO jobs within the platform. |
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