Powerful MLOps for Embedded and IoT Device Management

Increase speed and accuracy of Machine Learning with automated devices.

Simplify testing

The future of AI is Tiny

Every day we interact with devices that are rich with data that can help fuel the next generation of smart home, retail, healthcare, energy and autonomous driving. Harness it with MLOps.

This data is held back from AI by tremendous computer resource requirements. MLOps offers the future of devices that are efficient and accurate.

Gain the improved latency, scalability, security and safety of running AI on devices without the operational headaches.

Unlike most other automation platforms, BeetleboxCI has no requirements or software that needs to be placed on devices. This means that testing is incredibly resource efficient and allows developers to test the way they wish, using the software that they are familiar with.

Easily combine artificial intelligence with the IoT

Unparalleled performance for machine learning.

Through automation, designers can quickly evaluate every iteration of their work in a consistent and repeatable manner. This allows engineers to focus on their use-case with clear data results.

Improve scalability without adding to management complexity.

Managing fleets of devices running different machine learning models risks creating a device management nightmare. With BeetleboxCI, users can easily manage exactly what device are doing.

Secure and safe.

Easily monitor how models are performing in a safe and clearly tracked manner. Run BeetleboxCI on-premise or through private Cloud creating the necessary privacy needed for smart devices.

Reach a higher level of automation with BeetleboxCI

Grow from Level 0 to Level 2 MLOps and increase the velocity of your machine learning.

Level 0

Highly manual process where developers and data scientists are rapidly iterating, including data preparation and validation. IoT devices are directly connected to the developer’s desktop. Models are sufficient for experimentation but often break when confronted with the real world.

Level 1

Automated pipelines are introduced that enable training in a fully managed and traceable manner. Important data and validation steps occur throughout the process. Local test devices are connected to the pipeline to gain hardware performance benchmarks.

Level 2

Models are placed into production with close to zero down time with full system automation. Software is deployed across fleets of connected devices. The system is also able to retrieve new data from devices and use these to optimise models for future use.