IoT Management


IoT Management


IoT Management


CONNECT TO AND AGGREGATE ANY DATA SOURCE SUCH AS THE “INTERNET-OF-THINGS” TO TRANSFORM MORE DATA INTO ACTIONABLE INTELLIGENCE.

MACHINE LEARNING (ML) FOR IOT MANAGEMENT: WHAT IS IT? REALIZING IoT'S POTENTIAL WITH FULLY MANAGED IOT SERVICES WITH MACHINE LEARNING.

The growing number of IoT devices creates opportunities to convert increasing amounts of data into actionable intelligence. With AI-Surge, companies leverage data from connected objects to optimize their IoT Management, operational processes, and automate decision-making.

Industrial IoT Data Management, the new strategic challenge

Connected objects are now part of the daily lives of more and more consumers and businesses, with more than 21.5 billion items expected in 2025 by IoT Analytics. And the promises for economic activity are enormous, as suggested by this KPMG study published in 2018, telling us that IoT is ranked as the top driver for business transformation before artificial intelligence and robotics.

The proliferation of intelligent sensors, massive adoption of IoT devices, and integration of new applications raise questions about the amount of data generated by the connected objects and their processing for IoT Management.

Operationalization challenges of IoT Management and Intelligent Systems

However, a deployment strategy and a processing architecture adapted to IoT are often complicated to implement and bring many questions to the table. Business models, marketing of additional services, organization ... but especially the exploitation of the data, as the abundance and the nature of this one, are sources of new challenges: collection, frequency, availability, formatting, and update.

Data from connected objects put enormous pressure on information systems of companies that must pay particular attention to access to IoT data their security. They must learn to reduce downtimes and manage the critical flow data for optimized IoT Management.

AI-Surge brings Machine Learning to IoT devices

AI-Surge makes it possible to build an IoT Management value chain including IoT data in record time, without being slowed down by technical complexity, dedicated to a specific business use case identified upstream.

By providing, within a single tool, all the components necessary for the rapid deployment of IoT projects (connectivity, collection, storage, processing, and exposure), coupled with a capacity to handle large volumes of data in real-time, it is a natural accelerator to carry out an initiative integrating IoT. It includes:

  • An ability to integrate IoT data and to cross-reference them with data from other sources, external, internal, or open

  • A Platform as a Service backed up by cloud resources to handle any load without technical complexity

  • The possibility of integrating existing algorithms to capitalize on previous work

  • Alerts are issued when pre-defined limits are passed, keeping you informed in real-time of potential malfunctions

CONNECT TO AND AGGREGATE ANY DATA SOURCE SUCH AS THE “INTERNET-OF-THINGS” TO TRANSFORM MORE DATA INTO ACTIONABLE INTELLIGENCE.

MACHINE LEARNING (ML) FOR IOT MANAGEMENT: WHAT IS IT? REALIZING IoT'S POTENTIAL WITH FULLY MANAGED IOT SERVICES WITH MACHINE LEARNING.

The growing number of IoT devices creates opportunities to convert increasing amounts of data into actionable intelligence. With AI-Surge, companies leverage data from connected objects to optimize their IoT Management, operational processes, and automate decision-making.

Industrial IoT Data Management, the new strategic challenge

Connected objects are now part of the daily lives of more and more consumers and businesses, with more than 21.5 billion items expected in 2025 by IoT Analytics. And the promises for economic activity are enormous, as suggested by this KPMG study published in 2018, telling us that IoT is ranked as the top driver for business transformation before artificial intelligence and robotics.

The proliferation of intelligent sensors, massive adoption of IoT devices, and integration of new applications raise questions about the amount of data generated by the connected objects and their processing for IoT Management.

Operationalization challenges of IoT Management and Intelligent Systems

However, a deployment strategy and a processing architecture adapted to IoT are often complicated to implement and bring many questions to the table. Business models, marketing of additional services, organization ... but especially the exploitation of the data, as the abundance and the nature of this one, are sources of new challenges: collection, frequency, availability, formatting, and update.

Data from connected objects put enormous pressure on information systems of companies that must pay particular attention to access to IoT data their security. They must learn to reduce downtimes and manage the critical flow data for optimized IoT Management.

AI-Surge brings Machine Learning to IoT devices

AI-Surge makes it possible to build an IoT Management value chain including IoT data in record time, without being slowed down by technical complexity, dedicated to a specific business use case identified upstream.

By providing, within a single tool, all the components necessary for the rapid deployment of IoT projects (connectivity, collection, storage, processing, and exposure), coupled with a capacity to handle large volumes of data in real-time, it is a natural accelerator to carry out an initiative integrating IoT. It includes:

  • An ability to integrate IoT data and to cross-reference them with data from other sources, external, internal, or open

  • A Platform as a Service backed up by cloud resources to handle any load without technical complexity

  • The possibility of integrating existing algorithms to capitalize on previous work

  • Alerts are issued when pre-defined limits are passed, keeping you informed in real-time of potential malfunctions

CONNECT TO AND AGGREGATE ANY DATA SOURCE SUCH AS THE “INTERNET-OF-THINGS” TO TRANSFORM MORE DATA INTO ACTIONABLE INTELLIGENCE.

MACHINE LEARNING (ML) FOR IOT MANAGEMENT: WHAT IS IT? REALIZING IoT'S POTENTIAL WITH FULLY MANAGED IOT SERVICES WITH MACHINE LEARNING.

The growing number of IoT devices creates opportunities to convert increasing amounts of data into actionable intelligence. With AI-Surge, companies leverage data from connected objects to optimize their IoT Management, operational processes, and automate decision-making.

Industrial IoT Data Management, the new strategic challenge

Connected objects are now part of the daily lives of more and more consumers and businesses, with more than 21.5 billion items expected in 2025 by IoT Analytics. And the promises for economic activity are enormous, as suggested by this KPMG study published in 2018, telling us that IoT is ranked as the top driver for business transformation before artificial intelligence and robotics.

The proliferation of intelligent sensors, massive adoption of IoT devices, and integration of new applications raise questions about the amount of data generated by the connected objects and their processing for IoT Management.

Operationalization challenges of IoT Management and Intelligent Systems

However, a deployment strategy and a processing architecture adapted to IoT are often complicated to implement and bring many questions to the table. Business models, marketing of additional services, organization ... but especially the exploitation of the data, as the abundance and the nature of this one, are sources of new challenges: collection, frequency, availability, formatting, and update.

Data from connected objects put enormous pressure on information systems of companies that must pay particular attention to access to IoT data their security. They must learn to reduce downtimes and manage the critical flow data for optimized IoT Management.

AI-Surge brings Machine Learning to IoT devices

AI-Surge makes it possible to build an IoT Management value chain including IoT data in record time, without being slowed down by technical complexity, dedicated to a specific business use case identified upstream.

By providing, within a single tool, all the components necessary for the rapid deployment of IoT projects (connectivity, collection, storage, processing, and exposure), coupled with a capacity to handle large volumes of data in real-time, it is a natural accelerator to carry out an initiative integrating IoT. It includes:

  • An ability to integrate IoT data and to cross-reference them with data from other sources, external, internal, or open

  • A Platform as a Service backed up by cloud resources to handle any load without technical complexity

  • The possibility of integrating existing algorithms to capitalize on previous work

  • Alerts are issued when pre-defined limits are passed, keeping you informed in real-time of potential malfunctions