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Postgraduate Diploma in Deployment on Industrial Gateways and MLOps

Online 800-hour Postgraduate Diploma in Deployment on Industrial Gateways and MLOps. 8 modules covering IEC 62443-3-3, IEC 62443-4-2 and MLflow. Assigned tutor and open enrolment.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Postgraduate Diploma in Deployment on Industrial Gateways and MLOps — studying from India

India's automation and robotics market is growing with the expansion of manufacturing, automotive and electronics production. Industrial automation firms such as ABB, Siemens, Fanuc and Bosch operate in the country, alongside domestic integrators and start-ups in Bengaluru, Pune, Chennai and Hyderabad. Research institutions including the Indian Institute of Science and several IITs contribute to robotics and control research. Standards for industrial machinery and safety draw on the Bureau of Indian Standards. Engineers in this area design control systems, robotic cells, automation solutions and programmable logic controllers for factories and warehouses.

Fees for India: ₹137,000 (Indian Rupee), with instalments and employer sponsorship available.

Relevant bodies in India

  • Bureau of Indian Standards (BIS)
  • Indian Institute of Science (IISc)
  • Indian Institute of Technology (IIT) robotics research groups
  • Automation and Robotics Industry associations

Typical roles

  • Robotics Engineer
  • Control Systems Engineer
  • Automation Engineer
  • PLC Programmer

Questions from India

Is there a professional body in India that recognises robotics and automation engineers?

Robotics in India has no separate statutory title. The Institution of Engineers (India) is the main general engineering body, and technical standards come from the Bureau of Indian Standards. SEIUM's qualification is private and not accredited by any national body, so recognition rests with employers and the relevant body.

Which employers in India hire robotics and automation engineers?

Automotive plants, electronics manufacturers, factory automation suppliers, logistics firms, and robotics startups are typical employers. Hiring focuses on skills and project experience. The qualification can support a profile, but employers decide how much it counts and no outcome is guaranteed.

Can I study robotics online from India while working, and what language?

Yes, the programme is 100% online, so you can study from anywhere in India without a student visa and fit it around work. It is taught in English, so you need a reasonable level of the language. Each student has an assigned technical tutor, fees can be paid in instalments, and employer sponsorship is possible.

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Learning outcomes

What you will be able to do

01

Configure industrial gateways with containers and lightweight orchestration.

02

Design plant data ingestion pipelines using OPC UA and MQTT.

03

Implement the MLOps lifecycle with versioning and CI/CD.

04

Deploy models across edge fleets with secure updates.

05

Monitor data drift and model performance in production.

06

Apply the IEC 62443 series to edge deployments.

07

Plan the scaling of AI solutions across several plants.

Profile

Who it's for

  • Industrial MLOps engineer looking to specialise in Deployment on Industrial Gateways and MLOps.
  • IIoT solutions architect looking to specialise in Deployment on Industrial Gateways and MLOps.
  • Plant data engineer looking to specialise in Deployment on Industrial Gateways and MLOps.
  • OT DevOps engineer looking to specialise in Deployment on Industrial Gateways and MLOps.
  • Industrial cybersecurity specialist looking to specialise in Deployment on Industrial Gateways and MLOps.
  • Industrial data scientist looking to specialise in Deployment on Industrial Gateways and MLOps.
  • Engineering or science graduates seeking to enter discrete manufacturing.
Standards & tools

What you'll work with

  • IEC 62443-3-3
  • IEC 62443-4-2
  • Regulation (EU) 2024/1689
  • ISO/IEC 42001
  • IEC 62541
  • ISO/IEC 27001
  • ISO/IEC 5338
Syllabus

8 modules · 800 h

  1. 1.1Edge, fog and cloud architecture
  2. 1.2IIoT gateways and industrial PCs
  3. 1.3Industrial Linux operating systems
  4. 1.4Docker containers on the plant floor
  5. 1.5Lightweight orchestration with K3s
  6. 1.6Connectivity with PLCs and fieldbuses
  7. 1.7Computing resources and accelerators
  8. 1.8Environmental requirements for hardware
  9. 1.9Remote device management and telemetry
  10. 1.10Practical: commissioning a gateway with Docker
  1. 2.1Acquisition via OPC UA and Modbus
  2. 2.2MQTT and Sparkplug B messaging
  3. 2.3Time series and databases
  4. 2.4Data quality and synchronisation
  5. 2.5Data labelling in production
  6. 2.6Local and buffered storage
  7. 2.7Industrial data governance
  8. 2.8Data pipelines with Node-RED
  9. 2.9Standardising plant variable names
  10. 2.10Laboratory: OPC UA to InfluxDB pipeline
  1. 3.1MLOps principles and maturity levels
  2. 3.2Version control for data and models
  3. 3.3Experiment tracking with MLflow
  4. 3.4Model registry and metadata
  5. 3.5Reproducible training pipelines
  6. 3.6Continuous integration and delivery (CI/CD)
  7. 3.7Automated model testing
  8. 3.8Infrastructure as code
  9. 3.9Feature management with feature stores
  10. 3.10Workshop: training pipeline with MLflow
  1. 4.1Packaging models in containers
  2. 4.2Triton and TorchServe inference servers
  3. 4.3Deployment with Azure IoT Edge and AWS Greengrass
  4. 4.4Over-the-air (OTA) updates
  5. 4.5Canary and blue-green deployments
  6. 4.6Device fleet management
  7. 4.7Rollback and compatible versions
  8. 4.8Resource optimisation in inference
  9. 4.9Compatibility testing on the target hardware
  10. 4.10Practical: OTA deployment of a model across a fleet
  1. 5.1Service and model metrics
  2. 5.2Data and concept drift
  3. 5.3Drift detection with statistical tests
  4. 5.4Observability with Prometheus and Grafana
  5. 5.5Alerts and incident response
  6. 5.6Event-triggered retraining
  7. 5.7Feedback loop with operators
  8. 5.8Logging and auditing of predictions
  9. 5.9Service latency and availability metrics
  10. 5.10Laboratory: drift detection with Evidently
  1. 6.1IEC 62443 zones and conduits model
  2. 6.2Secure boot and root of trust
  3. 6.3Model signing and verification
  4. 6.4Secrets and certificate management
  5. 6.5Software supply chain security
  6. 6.6Adversarial attacks on models
  7. 6.7OT network segmentation
  8. 6.8Secure firmware updates
  9. 6.9Backup of models and configurations
  10. 6.10Case study: risk analysis of an IIoT gateway
  1. 7.1Organisation of plant data teams
  2. 7.2Managing technical debt in ML
  3. 7.3Operating costs of edge versus cloud
  4. 7.4Standards and regulatory compliance
  5. 7.5Documentation with model cards
  6. 7.6Scaling solutions across several plants
  7. 7.7Business value indicators
  8. 7.8Adoption roadmap
  9. 7.9Economic return of AI on the plant floor
  10. 7.10Case study: predictive maintenance scaling plan
  1. 8.1Use case definition
  2. 8.2Gateway architecture
  3. 8.3Plant data pipeline
  4. 8.4Model training and registration
  5. 8.5Edge deployment
  6. 8.6Monitoring and alerts
  7. 8.7Security plan
  8. 8.8Analysis of results
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, Docker and K3s, Azure IoT Edge, Node-RED, Grafana with Prometheus, in free, academic or evaluation versions. Case studies are based on real deployment procedures: deploying predictive maintenance models on gateways, monitoring drift in visual inspection, and managing multi-plant edge device fleets. Assessment is continuous per module, plus the final integrating project.

Accredited laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:faculty with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, OT cyber, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case definition

Configure industrial gateways with containers and lightweight orchestration.

Gateway architecture

Design plant data ingestion pipelines using OPC UA and MQTT.

Plant data pipeline

Implement the MLOps lifecycle with versioning and CI/CD.

Model training and registration

Deploy models across edge fleets with secure updates.

Edge deployment

Monitor data drift and model performance in production.

Career opportunities

Where this takes you

Industrial MLOps EngineerIIoT Solutions ArchitectPlant Data EngineerOT DevOps EngineerIndustrial Cybersecurity SpecialistIndustrial Data Scientist
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish-language proficiency to follow technical standards reading.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with full-time work.

You will work with MLflow, Docker and K3s, Azure IoT Edge, Node-RED, Grafana with Prometheus. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry of Education qualification.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the advanced programme. Check the recognition policy when you enrol.

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