Use Cases

Explore the transformative power of Expanso in data management, edge computing and machine learning deployment.

In this section, we showcase a selection of remarkable use cases that demonstrate how Expanso can revolutionize your approach to data processing and machine learning.

Log vending

Log vending
on device

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.

Solution

Expanso supports log vending on devices, eliminating the need for costly data transfers and compute to/on central servers.

Benefit

Save on expensive data transfer and cloud compute power costs, especially in scenarios where large volumes of logs need to be managed.

ML Inference on the Edge

ML inference
on the edge

Data governance frequently hinders the deployment of machine learning inference models on the edge and the model orchestration can be challenging.

Solution

With Expanso, you can effortlessly deploy machine learning models on edge devices for real-time inference.

Benefit

With Expanso, you can effortlessly deploy machine learning models on edge devices for real-time inference.

Cloud Image Search Across Geo Locations

Cloud image search across geo locations

Implementing a cloud image search while keeping data within your premises.

Solution

Expanso empowers you to train machine learning models right where your data is stored, eliminating the need for sensitive data to leave your premises.

Benefit

Gain full control over your data, streamline data governance, enhance data security, all while harnessing the advantages of cloud-based image search capabilities.

Distributed Fleet Management

Distributed fleet management

Fleet data is hard to access, has to be centralized for any usage, has additional security risks and is therefore costly to use.

Solution

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.

Benefit

Fleet data usage while reducing the economic, security and time cost.

Distributed Data Warehouse

Distributed data warehouse

Managing multiple data sources can hinder effective data utilization and often necessitates centralization.

Solution

Bacalhau offers a virtual data warehouse by federating queries across distributed data sources.

Benefit

Eliminate the need for central data storage and transfers, simultaneously enhancing cost efficiency and security.

Running Jobs Over Irregular Unreliable Network Connection

Running jobs over irregular / unreliable network connections

Unreliable network connections pose difficulties in data transfer and deployment of containers for software on edge devices.

Solution

Bacalhau employs decentralized job coordination to execute workloads robustly on unreliable edge networks.

Benefit

Achieve resilient deployment in challenging external conditions, making it an ideal solution for use in remote edge computing environments.

Federated Learning with Isolated Divisions

Federated learning with isolated divisions

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.

Solution

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.

Benefit

Companies achieve accurate ML models, minimize compliance risks, and enjoy controlled coordination for optimized learning.

Shared Server

Shared
server

Specialized computing resources are often a tradeoff between dedicated hardware cost and coordination/permission obstacles.

Solution

Bacalhau offers a decentralized platform for scheduling and coordinating experiments across shared GPU/TPU hardware, without transferring private code/data.

Benefit

Bacalhau optimizes hardware usage through sharing, accelerates research speed via scalable distributed access, and ensures data security, significantly benefiting cross-institutional collaborative efforts.