Real-World Impact with Expanso
Explore how Expanso transforms industries through innovative solutions, empowering businesses to optimize operations, enhance security, and drive success across diverse use cases.
Explore the transformative power of Expanso in data management, edge computing and machine learning deployment.
The current logs are sent uncompressed, are processed and saved in the cloud. A significant portion of the log data is unimportant, resulting in unnecessary bandwidth consumption and expensive compute power usage.
Expanso supports log vending on devices, eliminating the need for costly data transfers and compute to/on central servers.
Save on expensive data transfer and cloud compute power costs, especially in scenarios where large volumes of logs need to be managed.
Data governance frequently hinders the deployment of machine learning inference models on the edge and the model orchestration can be challenging.
With Expanso, you can effortlessly deploy machine learning models on edge devices for real-time inference.
Save on expensive data transfer and cloud compute power costs, especially in scenarios where large volumes of logs need to be managed.
Implementing a cloud image search while keeping data within your premises.
Expanso empowers you to train machine learning models right where your data is stored, eliminating the need for sensitive data to leave your premises.
Gain full control over your data, streamline data governance, enhance data security, all while harnessing the advantages of cloud-based image search capabilities.
Fleet data is hard to access, has to be centralized for any usage, has additional security risks and is therefore costly to use.
Bacalhau’s OSQuery integration and container execution provides a distributed SQL-like engine to query fleet data in-place and support for arbitrary, end-user specific binaries.
Fleet data usage while reducing the economic, security and time cost.
Managing multiple data sources can hinder effective data utilization and often necessitates centralization.
Bacalhau offers a virtual data warehouse by federating queries across distributed data sources.
Eliminate the need for central data storage and transfers, simultaneously enhancing cost efficiency and security.
Unreliable network connections pose difficulties in data transfer and deployment of containers for software on edge devices.
Bacalhau employs decentralized job coordination to execute workloads robustly on unreliable edge networks.
Achieve resilient deployment in challenging external conditions, making it an ideal solution for use in remote edge computing environments.
Companies face barriers in collaboratively training Machine Learning models across divisions separated by regulatory barriers. This hinders model development, with existing solutions either lacking global insights, leaking sensitive information, or compromising on data quality.
Bacalhau enables collaborative ML model training without the need to transfer raw data. Stringent data controls ensure that only the authorized insights are shared while effectively coordinating the learning process across divisions.
Companies achieve accurate ML models, minimize compliance risks, and enjoy controlled coordination for optimized learning.
Specialized computing resources are often a tradeoff between dedicated hardware cost and coordination/permission obstacles.
Bacalhau offers a decentralized platform for scheduling and coordinating experiments across shared GPU/TPU hardware, without transferring private code/data.
Bacalhau optimizes hardware usage through sharing, accelerates research speed via scalable distributed access, and ensures data security, significantly benefiting cross-institutional collaborative efforts.
The current logs are sent uncompressed, are processed and saved in the cloud. A significant portion of the log data is unimportant, resulting in unnecessary bandwidth consumption and expensive compute power usage.
Expanso supports log vending on devices, eliminating the need for costly data transfers and compute to/on central servers.
Save on expensive data transfer and cloud compute power costs, especially in scenarios where large volumes of logs need to be managed.
Data governance frequently hinders the deployment of machine learning inference models on the edge and the model orchestration can be challenging.
With Expanso, you can effortlessly deploy machine learning models on edge devices for real-time inference.
Save on expensive data transfer and cloud compute power costs, especially in scenarios where large volumes of logs need to be managed.
Implementing a cloud image search while keeping data within your premises.
Expanso empowers you to train machine learning models right where your data is stored, eliminating the need for sensitive data to leave your premises.
Gain full control over your data, streamline data governance, enhance data security, all while harnessing the advantages of cloud-based image search capabilities.
Fleet data is hard to access, has to be centralized for any usage, has additional security risks and is therefore costly to use.
Bacalhau’s OSQuery integration and container execution provides a distributed SQL-like engine to query fleet data in-place and support for arbitrary, end-user specific binaries.
Fleet data usage while reducing the economic, security and time cost.
Managing multiple data sources can hinder effective data utilization and often necessitates centralization.
Bacalhau offers a virtual data warehouse by federating queries across distributed data sources.
Eliminate the need for central data storage and transfers, simultaneously enhancing cost efficiency and security.
Unreliable network connections pose difficulties in data transfer and deployment of containers for software on edge devices.
Bacalhau employs decentralized job coordination to execute workloads robustly on unreliable edge networks.
Achieve resilient deployment in challenging external conditions, making it an ideal solution for use in remote edge computing environments.
Companies face barriers in collaboratively training Machine Learning models across divisions separated by regulatory barriers. This hinders model development, with existing solutions either lacking global insights, leaking sensitive information, or compromising on data quality.
Bacalhau enables collaborative ML model training without the need to transfer raw data. Stringent data controls ensure that only the authorized insights are shared while effectively coordinating the learning process across divisions.
Companies achieve accurate ML models, minimize compliance risks, and enjoy controlled coordination for optimized learning.
Specialized computing resources are often a tradeoff between dedicated hardware cost and coordination/permission obstacles.
Bacalhau offers a decentralized platform for scheduling and coordinating experiments across shared GPU/TPU hardware, without transferring private code/data.
Bacalhau optimizes hardware usage through sharing, accelerates research speed via scalable distributed access, and ensures data security, significantly benefiting cross-institutional collaborative efforts.
For enterprises navigating the complexities of vast data, Bacalhau is the definitive answer.
For organizations seeking enhanced benefits, including robust binaries, SLAs, and dedicated support.
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