Coiled provides a platform that enables data scientists to scale Python applications using Dask for parallel computing, allowing them to process large datasets from 10 GiB to 100 TiB without requiring cloud expertise. The solution simplifies access to GPU resources and ephemeral virtual machines, facilitating efficient data transformation and machine learning workflows directly from local environments.
Funding
$26M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data scientists often face challenges when scaling Python applications for large datasets, as traditional methods require significant cloud expertise and can be cumbersome to manage. Processing datasets ranging from 10 GiB to 100 TiB necessitates efficient access to GPU resources and virtual machines, which can be difficult to provision and configure.
Solution
Coiled provides a platform that simplifies the scaling of Python applications using Dask for parallel computing, enabling data scientists to process large datasets without requiring extensive cloud infrastructure knowledge. The platform offers streamlined access to GPU resources and ephemeral virtual machines, facilitating efficient data transformation and machine learning workflows directly from local environments. By automating the deployment and management of Dask clusters, Coiled allows users to focus on data analysis and model development rather than infrastructure management. The platform supports various Python-based data science tools, including Pandas, Xarray, and Scikit-learn, enabling users to leverage their existing skills and workflows.
Target Audience
Coiled is designed for data scientists and machine learning engineers who need to scale Python applications for large datasets but lack extensive cloud infrastructure expertise.
Features
- Automated Dask cluster deployment and management on cloud infrastructure
- Simplified access to GPU resources for accelerated computing
- Ephemeral virtual machines that match local environments, ensuring code compatibility
- Support for processing datasets ranging from 10 GiB to 100 TiB
- Integration with popular Python data science libraries, such as Pandas, Xarray, and Scikit-learn
- Serverless functions for parallelizing Python code without infrastructure management
- Optimized for data transformation, machine learning, and geospatial analysis workflows
- Cost optimization strategies, including spot instance utilization and ARM-based instance support