AWS Marketplace

How to Unlock Real-Time Industrial Intelligence with AI-Driven AWS IIoT

Manufacturers today face a critical challenge: transforming vast volumes of raw machine data into actionable insights. As industrial operations become increasingly digitized, AWS customers often ask how to combine edge connectivity, real-time analytics, and AI to optimize equipment effectiveness and predict downtime across factory networks.

In this post, we explore how to implement the Innova Solutions Industrial IoT (IIoT) offering, available in AWS Marketplace, to achieve these goals. You’ll learn how to connect factory floor machines to AWS using edge gateways, ingest and normalize industrial data in a centralized data lake, apply AI/ML for predictive maintenance and anomaly detection, deliver real-time performance dashboards, and empower operators with generative AI – all while aligning with AWS Well-Architected Framework best practices.

The AWS Well-Architected Framework empowers organizations to build cloud solutions that excel across critical pillars such as security, performance, operational efficiency, and cost optimization. Solutions built on this framework follow AWS best practices and incorporate features like automated monitoring and proactive issue detection to reduce operational overhead. By adopting Well-Architected principles, organizations can shift their focus from infrastructure management to innovation and growth – leveraging a cloud environment designed to evolve and improve over time.

A few prerequisites are required to deploy the solution effectively:

Solution Architecture

Complete AWS IoT industrial solution architecture showing data flow from OPC-DA/UA devices through AWS services to analytics and storage

Figure 1. AWS event-driven architecture diagram illustrating real-time data processing pipeline with serverless components and analytics

Connecting Industrial Devices at the Edge

The first step in enabling real-time intelligence is establishing secure connectivity between factory floor equipment and AWS. The Innova edge gateway, deployed on-premises, facilitates this connection by integrating with protocols like OPC-UA and MQTT via Kepware. AWS IoT Greengrass supports local processing and offline operation, ensuring resilience even during network disruptions.

Secure device communication is established through AWS IoT Core, using device certificates and managed policies. For enhanced security and compliance, AWS IoT Device Defender can be used to monitor device behavior and audit configurations.

Building a Scalable Industrial Data Lake

Once connectivity is established, the next focus is on data ingestion and normalization. Industrial data is streamed to AWS IoT Core, where routing rules direct it to downstream services. Lightweight transformations are applied using AWS Lambda, and data is stored in Amazon S3 in raw, cleansed, and enriched formats.

AWS Glue is used to discover schema, perform ETL operations, and catalog metadata, while AWS Lake Formation ensures secure access control and governance. This architecture creates a centralized, scalable data lake that serves as the foundation for analytics and AI.

Visualizing Performance Across the Factory

Operational visibility is critical for decision-making. The Innova IIoT solution integrates with Amazon QuickSight to deliver executive-level dashboards and KPIs, while Amazon Managed Grafana supports engineering and operations teams with detailed performance views. For real-time operator interfaces, Node-RED HMI provides intuitive visualizations on the factory floor.

Metrics such as Overall Equipment Effectiveness (OEE), downtime frequency, and throughput trends are tracked across machines, shifts, and plants – enabling stakeholders to act on insights immediately.

Applying Predictive AI/ML with Amazon SageMaker

Use Amazon SageMaker to train and deploy predictive models that help prevent unplanned machine failures and optimize production planning and resource allocation. Begin by preprocessing time-series sensor data and training models such as XGBoost and LSTM within the Amazon SageMaker Studio. For real-time inference, deploy endpoints in the cloud or use SageMaker Edge Manager to run models directly at the edge.

To forecast key performance indicators (KPIs) like throughput, energy consumption, and Overall Equipment Effectiveness (OEE), leverage time-series models such as Prophet and ARIMA. Manage the full model lifecycle with SageMaker Pipelines for automation and monitor model drift using SageMaker Model Monitor, ensuring consistent performance and reliability over time.

Enable generative AI with Amazon Bedrock

Go beyond prediction and unlock deeper operational insights with Amazon Bedrock. Empower your teams with AI copilots designed for industrial data – reducing operator training time, accelerating root cause analysis, and enhancing decision-making on the factory floor.

Amazon Bedrock enables intuitive, natural language interactions, allowing operators to ask questions like, “Why did Machine 7 fail yesterday?” and receive context-aware responses. It can automatically generate dynamic standard operating procedures (SOPs) based on real-time conditions and produce AI-generated diagnostic summaries to highlight root causes.

The solution architecture leverages Anthropic’s Claude and Amazon Titan Foundation Models within Amazon Bedrock. For document retrieval, it integrates Amazon Kendra, and for advanced prompt orchestration, it uses LangChain or Amazon Bedrock Agents – creating a seamless, intelligent experience for industrial teams.

Monitoring and Governance with Amazon CloudWatch

System health and performance are monitored using Amazon CloudWatch. Key metrics include data ingestion rates, model inference latency, device connectivity status, and anomaly thresholds. Alerts and dashboards enable proactive issue resolution and ensure continuous compliance with AWS Well-Architected best practices.

Clean up

After completing the walkthrough, remove any deployed resources to avoid ongoing charges:

  1. Edge & IoT Resources: Stop Innova Edge Gateway instances, deregister devices from AWS IoT Core, and delete any related Things, Certificates, Rules, or IoT Greengrass groups.
  2. Data Lake Assets: Empty or delete Amazon S3 buckets and remove related AWS Glue catalogs and Lake Formation permissions.
  3. Analytics & Dashboards: Delete Amazon QuickSight, Grafana, Timestream, or Managed Service for Apache Flink resources created for visualization or analytics.
  4. AI/ML Components: Remove Amazon SageMaker models, endpoints, pipelines, and any Amazon Bedrock or Kendra resources used for generative AI.
  5. Monitoring & Alerts: Delete Amazon CloudWatch, EventBridge, and SNS alarms, dashboards, and custom metrics.
  6. Marketplace Solution: If deployed via AWS Marketplace, unpublish or terminate the Innova IIoT product instance.

Conclusion

Following AWS Well-Architected Framework best practices, the Innova IIoT solution supports reliability, operational excellence, and performance efficiency by continually monitoring critical components so that new deployments meet architectural standards and alerts are generated before issues impact business operations. With AWS services such as Amazon SageMaker, Amazon Bedrock, and Amazon S3, manufacturers can unlock real-time operational intelligence, reduce downtime, and provide workers with AI-driven insights.

Explore the Innova IIot solution in AWS Marketplace to get started. To connect with Innova, choose Request private offer in AWS Marketplace. Our team will reach out to understand your specific requirements and guide you through the next steps.

About the authors

Ryan Dsouza

Ryan Dsouza is a principal solutions architect in the Cloud Optimization organization at Amazon Web Services (AWS). Based in New York City, Ryan helps customers design, develop, and operate more secure, scalable, and innovative solutions using the breadth and depth of AWS capabilities to deliver measurable business outcomes. He is actively engaged in developing strategies, guidance, and tools to help customers architect solutions that optimize for performance, cost-efficiency, security, resilience, and operational excellence, adhering to the AWS Cloud Adoption Framework and AWS Well-Architected Framework

Priyanka Sanjeev

Priyanka Sanjeev is a technical program manager in the Cloud Optimization organization at Amazon Web Services (AWS). Based in Seattle, Priyanka spearheaded from concept to deliver the Well-Architected Validated Solutions initiative, in which mechanisms such as automated reviews and remediations and enablement of the Well-Architected Framework were integrated into the solution build and delivery lifecycle. Solutions built following these principles stay Well-Architected through the lifecycle of the workload

Ravi Krishnamurthy

Ravi Krishnamurthy is a seasoned technology leader based in Atlanta, where he has lived for over 20 years. He holds a master’s degree in computer science with a specialization in human-computer interaction and AI. As senior director at Innova, Ravi leads the enhancement of enterprise capabilities and innovative solutions that drive measurable client success. He has presented thought leadership on global stages, including showcasing an Industrial IoT (IIoT) customer success story at AWS re:Invent. Ravi is an AWS Certified Solutions Architect – Professional, AWS Certified Solutions Architect – Associate, and a Terraform Certified Professional, bringing deep expertise in cloud architecture, AI, and modern infrastructure automation. Passionate about bridging innovation with real-world impact, he continues to shape forward-looking strategies that empower organizations to scale and succeed