Edge AI for Manufacturing: Real-Time Intelligence at the Device Level Treningskurs

Denne instruktørledede, liveopplæringen i Norge (online eller på stedet) er rettet mot AI-utøvere, forskere og utviklere på avansert nivå som ønsker å mestre de siste fremskrittene i Edge AI, optimalisere AI-modellene deres for edge-distribusjon og utforske spesialiserte applikasjoner på tvers av ulike bransjer.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Utforsk avanserte teknikker i Edge AI-modellutvikling og -optimalisering.
• Implementer banebrytende strategier for å distribuere AI-modeller på avanserte enheter.
• Bruk spesialiserte verktøy og rammeverk for avanserte Edge AI-applikasjoner.
• Optimaliser ytelsen og effektiviteten til Edge AI-løsninger.
• Utforsk innovative brukstilfeller og nye trender i Edge AI.
• Ta tak i avanserte etiske og sikkerhetsmessige hensyn i Edge AI-implementeringer.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot utviklere på mellomnivå, dataforskere og teknologientusiaster som ønsker å få praktiske ferdigheter i å distribuere AI-modeller på avanserte enheter for ulike applikasjoner.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå prinsippene til Edge AI og dens fordeler.
• Sett opp og konfigurer edge computing-miljøet.
• Utvikle, trene og optimalisere AI-modeller for edge-distribusjon.
• Implementer praktiske AI-løsninger på edge-enheter.
• Evaluer og forbedre ytelsen til edge-distribuerte modeller.
• Ta tak i etiske og sikkerhetsmessige hensyn i Edge AI-applikasjoner.

AI-powered predictive maintenance applies machine learning and data analytics to forecast equipment failures and optimize maintenance schedules. It transforms reactive maintenance models into proactive strategies, enabling better uptime, cost reduction, and asset longevity.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement AI-driven predictive maintenance solutions in industrial environments.

By the end of this training, participants will be able to:

• Understand how predictive maintenance differs from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-powered analysis.
• Apply machine learning models to detect anomalies and predict failures.
• Implement end-to-end workflows from sensor data to actionable insights.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises and case studies.
• Live demonstration and practical data workflows.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI for Process Optimization is the application of machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing operations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level manufacturing professionals who wish to apply AI techniques to streamline operations, reduce downtime, and support continuous improvement initiatives.

By the end of this training, participants will be able to:

• Understand AI concepts relevant to manufacturing optimization.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to support data-driven decisions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI for Quality Control is the use of computer vision and machine learning techniques to identify defects, anomalies, and deviations in production processes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level quality professionals who wish to apply AI tools to automate inspections and improve product quality in manufacturing environments.

By the end of this training, participants will be able to:

• Understand how AI is applied in industrial quality control.
• Collect and label image or sensor data from production lines.
• Use machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI in Supply Chain and Manufacturing Logistics is the application of predictive analytics, machine learning, and automation to optimize inventory, routing, and demand forecasting.

This instructor-led, live training (online or onsite) is aimed at intermediate-level supply chain professionals who wish to apply AI-driven tools to enhance logistics performance, forecast demand accurately, and automate warehouse and transport operations.

By the end of this training, participants will be able to:

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transport using AI-based techniques.
• Automate decision-making in warehouses and fulfillment processes.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI in Smart Factories is the application of artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led, live training (online or onsite) is aimed at beginner-level decision-makers and technical leads who wish to gain a strategic and practical introduction to how AI can be leveraged in smart factory environments.

By the end of this training, participants will be able to:

• Understand the core principles of AI and machine learning.
• Identify key AI use cases in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimization.
• Evaluate the steps involved in launching AI-driven initiatives.

Format of the Course

• Interactive lecture and discussion.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI Use Case Implementation is a hands-on, project-driven approach to applying machine learning, computer vision, and data analytics to solve real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (online or onsite) is aimed at intermediate-level cross-functional teams who wish to collaboratively implement AI use cases aligned with their operational goals and gain experience working with industrial data pipelines.

By the end of this training, participants will be able to:

• Select and scope practical AI use cases from operations, quality, or maintenance.
• Work collaboratively across roles to develop machine learning solutions.
• Handle, clean, and analyze diverse industrial datasets.
• Present a working prototype of an AI-enabled solution based on a selected use case.

Format of the Course

• Interactive lecture and discussion.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Digital Twins are virtual replicas of physical systems enhanced by real-time data and AI-driven intelligence.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to build, deploy, and optimize digital twin models using real-time data and AI-based insights.

By the end of this training, participants will be able to:

• Understand the architecture and components of digital twins.
• Use simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behavior and anomaly detection.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot utviklere på middels nivå og IT-fagfolk som ønsker å få en omfattende forståelse av Edge AI fra konsept til praktisk implementering, inkludert oppsett og distribusjon.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå de grunnleggende konseptene til Edge AI.
• Sett opp og konfigurer Edge AI-miljøer.
• Utvikle, trene og optimalisere Edge AI-modeller.
• Distribuer og administrer Edge AI-applikasjoner.
• Integrer Edge AI med eksisterende systemer og arbeidsflyter.
• Ta tak i etiske hensyn og beste praksis i Edge AI-implementering.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot helsepersonell på middels nivå, biomedisinske ingeniører og AI-utviklere som ønsker å utnytte Edge AI for innovative helsetjenester.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå rollen og fordelene med Edge AI i helsevesenet.
• Utvikle og distribuer AI-modeller på avanserte enheter for helseapplikasjoner.
• Implementer Edge AI-løsninger i bærbare enheter og diagnoseverktøy.
• Design og distribuer pasientovervåkingssystemer ved hjelp av Edge AI.
• Ta tak i etiske og regulatoriske hensyn i AI-applikasjoner for helsetjenester.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot utviklere på middels nivå, systemarkitekter og bransjefolk som ønsker å utnytte Edge AI for å forbedre IoT-applikasjoner med intelligente databehandlings- og analysefunksjoner.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå det grunnleggende om Edge AI og dens anvendelse i IoT.
• Sett opp og konfigurer Edge AI-miljøer for IoT-enheter.
• Utvikle og distribuer AI-modeller på edge-enheter for IoT-applikasjoner.
• Implementere sanntids databehandling og beslutningstaking i IoT-systemer.
• Integrer Edge AI med ulike IoT-protokoller og plattformer.
• Ta tak i etiske hensyn og beste praksis i Edge AI for IoT.

Industrial computer vision with AI is transforming how manufacturers and QA teams detect surface defects, verify part conformity, and automate visual inspection processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level QA teams, automation engineers, and developers who wish to design and implement computer vision systems for defect detection and inspection using AI techniques.

By the end of this training, participants will be able to:

• Understand the architecture and components of industrial vision systems.
• Build AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimize AI-powered inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot utviklere på nybegynnernivå og IT-fagfolk som ønsker å forstå det grunnleggende om Edge AI og dens introduksjonsapplikasjoner.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå de grunnleggende konseptene og arkitekturen til Edge AI.
• Sett opp og konfigurer Edge AI-miljøer.
• Utvikle og distribuer enkle Edge AI-applikasjoner.
• Identifiser og forstå brukstilfellene og fordelene med Edge AI.

Smart Robotics is the integration of artificial intelligence into robotic systems for improved perception, decision-making, and autonomous control.

This instructor-led, live training (online or onsite) is aimed at advanced-level robotics engineers, systems integrators, and automation leads who wish to implement AI-driven perception, planning, and control in smart manufacturing environments.

By the end of this training, participants will be able to:

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for collaborative and industrial robots.
• Deploy learning-based control strategies for real-time decision making.
• Integrate intelligent robotic systems into smart factory workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.