The document discusses Red Hat's edge computing solutions for industrial use cases. It provides an overview of edge computing and the different levels in a manufacturing edge deployment. It then describes Red Hat's approach to edge, which utilizes technologies like Red Hat OpenShift, Apache Camel K, and Red Hat OpenShift Data Science. The rest of the document demonstrates a condition monitoring use case, shows a Camel K integration demo, and provides an overview of Red Hat OpenShift Data Science for machine learning model development, deployment, and monitoring.

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Transforming manufacturing with
Red Hat Edge solutions
Industrial Edge
Arthy Loganathan
Vaishnavi R
Shravani Patnamshetty

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Agenda
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● Overview of Edge Computing
● The Red Hat Approach to Edge
● Technologies used for Industrial Edge Use case
● Red Hat Integration
● Red Hat Openshift Data Science
● Experience the edge AI/ML demonstration for manufacturing
● Demo

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Overview of Edge Computing
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Overview of Edge Computing
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Device
Actuator, Sensor, PLC
~100.000
Line Server
IPC, PLC, HMI, SCADA
~1000
Plant Data Center
SCADA, PPS, MES, PLM
~100
Regional Data Center
PPS, MES, PLM, ERP
~20-50
Headquarter / Cloud Data Center
MES, PLM, ERP
~1-3
EDGE CORE
Example: Manufacturing Edge
Level 0-1 Level 1-2 Level 2-3 Level 3-4 Level 4
Levels as defined by Purdue/ANSI/ISA-95
Code, Configuration, Master Data, ML Models, etc.
Sensor Data, Events, Operational Data, General Information, etc.

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Types Of Edge
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Day to Day Edge Operational Challenges
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The Red Hat Approach to Edge
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● Red Hat’s broad portfolio provides the connectivity, integration, and infrastructure as the basis for the platform,
application, and developer services.
● These powerful building blocks enable customers to solve their most challenging use cases.

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Edge Computing with AI/ML
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● Support faster decisions and actions in the plant.
● Proactively discover potential errors at the assembly line.
● Reduce equipment downtime through predictive maintenance.
● Boost product quality.

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Technologies used for Industrial Edge Use case
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Use Case:
Condition Monitoring
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Factory data center
Line Data Server
Central data center
MQTT
Fieldbus
Device Level
IoT Edge Framework
- API access to hardware interfaces
- field protocols ( Modbus, OPC-UA, S7)
Sensor
Simulator
Sensor
Simulator
Kafka
Messaging
Consumer
Camel-k
IoT Dashboard
Sensor metrics,
threshold, alarms
MQTT Rec
[RH AMQ]
Anomaly
Detection
[Seldon Core]
ML Model
Kafka
Mirror Maker
ML Model
Training
OpenDataHub
App-Dev
(CloudeIDE, Pipeline)
S3
[OCS]

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Serverless Integration on
Kubernetes with Apache Camel K

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Apache Camel
● One of the most active Apache Software Foundation projects
● +300 components
○ Several components contributed by the community
● Many sub-projects:
○ Camel Quarkus
○ Camel Spring-Boot
○ Camel Kafka Connect
○ Camel K
● https://camel.apache.org
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Apache Camel
Swiss knife of integration
Solve integration problem
by applying best practices
out of the box. Even with
microservice architectures.
Patterns
Translate messages in
multiple formats, and
industry standard formats
from finance, telco, health-
care, and more
Data Formats
Quarkus, Standalone,
Spring Boot,Application
Servers, and natively on
Cloud.
Lightweight Runtimes
Packed with 300+
components such as
databases, message
queues, APIs.
300+ Components
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Apache Camel K
A lightweight integration platform based on Apache Camel, born on Kubernetes, with serverless
superpowers
https://landscape.cncf.io/serverless
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Apache Camel K
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Camel K for Developers
CREATE
integration file
from("knative:event/xxxx")
.transform()...
.to("kafka:topic")
1 EXECUTE
CLI Tools
$ kamel run integration.java
2 RUNNING
on Kubernetes
3
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Camel K Architecture
Knative
Kubernetes/OpenShift
Camel K Operator
DEVELOPER
Tailored for cloud-native development
experience:
● Built-in dependency management
● Rapid deployment, incremental updates
● Automate cloud resources generation
● Highly configurable
● Live coding on cloud
LIVE
UPDATE!
KEDA
Integration Pods
Integration
Custom Resource
...
RUN
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DEMO
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OpenShift Data Science

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Overview of Red Hat OpenShift Data Science
Core components of AI/ML workloads
Activities related to data
acquisition, data protection,
data auditability, data prep,
and data cataloging
Model training and model
evaluation (hyperparameter
tuning, model testing)
Deploying trained models and
capturing metrics that can be
used to evaluate model
performance
Monitoring model
performance and deploying
pipelines to automate
machine learning workflows
Data Engineering Model Development Model Serving Model Monitoring & Lifecycle

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Model Life Cycle
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● ML as a service platform for Managed Cloud service as well as for self
managed portfolio.
● Provides a sandbox environment for data scientists to develop, train and
test machine learning models and deploy them using Model Serving.
● Includes Jupyter Notebooks & core AI/ML libraries (TensorFlow, PyTorch)
● Optional third party AI/ML tools offered by software and hardware
partners
Overview of Red Hat Openshift Data Science
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Overview of Red Hat OpenShift Data Science
MLOps platform
Model development
Conduct exploratory data science in JupyterLab with access to
core AI / ML libraries and frameworks including TensorFlow and
PyTorch using our notebook images or your own.
Collaborate within a common
platform to bring IT, data science,
and app dev teams together
Model serving & monitoring
Deploy models across any cloud, fully managed, and self-
managed OpenShift footprint and centrally monitor their
performance.
Hardware acceleration
Accelerate your data science experiments through the use of
CPU and GPU acceleration on Red Hat OpenShift.
Partner ecosystem
Optional integrated ISVs to complement core platform
capabilities with access to the full partner ecosystem with AI/ML
technology partners.
Now GA as fully managed cloud service
and self-managed product

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● TrustyAI
● Integration with Project Wisdom
● Data Science Pipelines
Next Steps …
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DEMO
https://drive.google.com/file/d/1Gbcw1tpr-
0T_h8nkqQR9kSphaV5xQQOM/view?usp=sharing
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Thank you
Questions?
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