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Industrial Automation

Manage and maintain critical energy assets, components and infrastructure to extend remaining useful life and improve energy forecasts to optimise performance and grid demand and availability.

A problem we solve in this market .

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Indsutry potential

Network independent devices
Share learning without moving data
Preserving data privacy
with >50x network and cloud reduction
resilient to network bottlenecks Iot data dynamics
Resulting in better AI performance and robustness

Greater accuracy

x2

Reduction in communication overhead costs x50

x50

Reduction in wall to wall learning times

x10

Fixed per hour CPU cost £0.60

£0.60

Fixed OP infrastructure cost £150

£150

Reduction in cloud CPU/GPU cost x100

x100

AI team overhead saving

x3-4

TCO reduction x10

x10

Use case .

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Problem

Learn to predict machine stoppages with high frequency data from across customers. Customers are IP sensitive and will not share data to the OEM.

Solution

Generate, deploy, and manage Edge AI using privacy preserving Federated Learning Ops with OctaiPipe using an infrastructure that is economically profitable.

Impact

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5% reduction of spoilage events
Increase revenue by ~£3.9M per line per year
5x more profitable with octaipipe vs. cloud

Requirements.

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Rapid Deployment

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Cloud agnostic

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Edge-Cloud connectivity

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Model management

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Automated, scalable deployment

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AI managed fl-ops

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Federated learning

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Optimised for micro computers

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Explore our reference architecture for the manufacturing industry

View documentation

Further applications

Only model parameters are shared from edge devices on the network, which are aggregated and encrypted. This ensures data confidentiality and avoids any risk involved with the transfer or large volumes of sensitive data.

Primary Secondary

Regressor compatible

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Classification

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Cluster and smoothing

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Model management

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Model versioning

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

FAQs

01

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02

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03

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04

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05

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Water

Manage and maintain critical energy assets, components and infrastructure to extend remaining useful life and improve energy forecasts to optimise performance and grid demand and availability.

applied attributes to that industry
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