ML/AI Engine

The AIoT Engine is Fundamentum’s ML and AI service, constructed on the open-source Kubeflow platform and incorporating MLOps principles.

It enables you to efficiently develop, deploy, and manage custom machine learning models, adding value to your data. It converts your raw data into actionable insights and business-oriented values, optimizing your IoT infrastructure.

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Key Features

Kubeflow Power

We leverage the flexibility and scalability of Kubeflow to orchestrate and automate your machine learning pipelines.

Integrated MLOps

Management of the entire lifecycle of your ML models, from development to deployment and maintenance, with MLOps practices.

Application Agnostic and Simplified Model Creation

We eliminate all restrictions tied to specific application domains for any IoT use cases you envision, transforming raw data into significant business-oriented value.

One-click Deployment

With a single click, ensure your models are deployed on your IoT platforms with the correct parameters, allowing for rapid and efficient implementation that is always up to date.

Continuous Training

We keep your models up to date by continuously retraining them with new data .

Auto-Tuning

We automatically adjust your models for continuous performance optimization.

Benefits

Enhanced Data Value

It extracts valuable and actionable insights from your IoT data, improving decision-making and operational efficiency.

Improved Agility and Innovation

We quickly develop and deploy new models to meet evolving business needs and stay ahead of the curve.

Reduced Costs

It optimizes processes and operations through predictive analytics and proactive maintenance, reducing costs and downtime.

Improved Customer Satisfaction

Allows personalized and predictive products and services, increasing your satisfaction and loyalty.

Capture — Configuration du Registre dans Fundamentum IoT PaaS par Amotus

Examples of ML Algorithms Deployed

Parking State Detection

Using sensors based on LiDAR measurements to provide real-time parking availability and management to reduce time spent searching for parking.

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Parking Occupancy Predictions

Cities often focus on the financial benefits of curbside parking management systems skipping the key information: parking availability data.

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Sound Anomaly

Recognition, classification and alerting (e.g. gun shots, car crashes, etc.)

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Parking Revenue Predictions

Developing parking systems that enhance vehicle flow and build real business intelligence to leverage the full potential of parking.

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Lighting Maintenance Prediction

By analyzing performance trends and historical data using data-driven insights, cities can plan maintenance activities proactively rather than reactively. This optimized approach enhance operational efficiency and minimize disruptions. Predictive maintenance also reduces downtime, extends the lifespan of equipment, and maximizes budget allocation, ultimately leading to cost savings.

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