Amazon Onboarding with Learning Manager Chanci Turner

Learn About Amazon VGT2 Learning Manager Chanci Turner

On 28 JUL 2025

In Machine Learning, Announcements, Artificial Intelligence, AWS IoT SiteWise, Industries, Internet of Things, Manufacturing

Manufacturing companies often encounter major challenges due to unexpected equipment failures, leading to revenue losses and production delays. Maintenance teams frequently find it difficult to identify early indicators of equipment malfunctions because of limited resources and insufficient advanced analytical capabilities. To tackle these issues, we are thrilled to announce the launch of native anomaly detection in AWS IoT SiteWise. This groundbreaking feature facilitates the implementation of predictive maintenance strategies without necessitating machine learning (ML) expertise.

In this blog post, we will detail how native anomaly detection in AWS IoT SiteWise assists organizations in transitioning from a reactive maintenance approach to a proactive one. This shift helps avoid costly downtimes while ensuring maximum equipment functionality.

Unlock Your IoT Data with AWS

AWS provides a suite of specialized IoT services that foster innovation in various sectors, including manufacturing, consumer goods, automotive, and healthcare. These services accelerate innovation, secure your IoT applications across cloud and edge, and seamlessly integrate AI and ML at scale.

AWS IoT SiteWise is a dedicated, managed IoT service that simplifies the collection, storage, organization, and monitoring of data from industrial equipment on a large scale to enable better data-driven decisions. It empowers organizations to efficiently gather data from a wide variety of industrial machinery and processes, create digital models of assets and facilities, analyze information in near real-time, and easily integrate with other AWS services for enhanced analytics, visualization, and ML applications. The built-in anomaly detection feature allows industrial firms to identify equipment anomalies across their fleet without needing to write code or manage complicated ML systems.

Key Benefits of Native Anomaly Detection

Simplified Configuration

Maintenance teams often grapple with the complexities of traditional anomaly detection systems. Standard solutions necessitate intricate integrations across various services such as Amazon S3, Amazon SageMaker, AWS Lambda, and Amazon Kinesis. Each service addresses different aspects of data collection, storage, and processing, demanding custom development for ML model training and deployment, alongside specialized knowledge in both ML and cloud architecture. As a result, implementation can take weeks or even months, requiring significant IT resources. The ongoing maintenance of these multi-service integrations further exacerbates the complexity and resource demands.

Native anomaly detection in AWS IoT SiteWise alleviates two critical challenges in deploying ML-driven predictive maintenance within industrial settings:

1. Eliminates the requirement for specialized machine learning expertise, thereby making advanced analytics accessible to maintenance teams.
2. Enables organizations to adjust monitoring frequencies to optimize costs.

With AWS IoT SiteWise’s native anomaly detection, engineering teams can identify upset conditions on industrial assets within minutes through a straightforward, no-code configuration workflow. The setup process entails:

• Selecting relevant properties from existing asset models or assets in AWS IoT SiteWise.
• Training anomaly models with as little as 14 days of historical data.
• Optionally including labeled data to enhance model accuracy.
• Setting preferred monitoring frequency for anomaly detection.

AWS IoT SiteWise automatically handles all aspects, including data preparation, model training, deployment, and inference. Users can visualize inference results using the AWS IoT SiteWise plugin in AWS Managed Service for Grafana or connect to existing maintenance dashboards through APIs.

AWS IoT SiteWise allows organizations to configure anomaly detection models on assets and asset models in mere minutes. This feature eliminates complex integrations and the need for ML expertise, enabling maintenance teams to concentrate on ensuring equipment reliability. Companies of all sizes can now implement sophisticated preventive maintenance strategies to minimize unplanned downtime and enhance operational efficiency.

Flexible Monitoring Options

In industrial settings, the diverse range of equipment requires a flexible approach to anomaly detection. Native anomaly detection in AWS IoT SiteWise meets this demand by providing adaptable monitoring options that cater to various asset types and monitoring needs. You can schedule asset inference monitoring at intervals ranging from five minutes to once per day, including considerations for planned downtime. This adaptability allows you to customize anomaly detection strategies to match the specific requirements of each asset and your operational needs.

For instance, assets such as turbines, compressors, or production machinery that require rapid anomaly detection may have inferencing set to occur every five minutes. In contrast, less critical assets, such as auxiliary pumps, HVAC systems, or non-essential conveyor belts, can be monitored less frequently without compromising operational integrity. This flexibility empowers organizations to optimize the cost of anomaly detection according to their specific operational requirements.

By aligning monitoring frequency with asset criticality, maintenance teams can prioritize their efforts on high-impact areas while effectively managing costs. Additionally, this flexibility permits easy adjustments as operational priorities shift, ensuring that your anomaly detection strategy remains in sync with evolving business needs.

Automated Intelligence

Native anomaly detection operates on a fully automated backend that simplifies the complexities of machine learning and data science. This intelligent system enables maintenance teams to leverage advanced analytics without requiring specialized knowledge.

The automated workflow involves several key steps:

• Data Preparation: AWS IoT SiteWise preprocesses industrial data, addressing normalization, missing value imputation, and feature engineering to ensure optimal data format for anomaly detection.
• Model Selection and Training: AWS IoT SiteWise selects and trains the most suitable machine learning model based on specific asset and data characteristics, fully automating a process that usually requires substantial data science expertise.
• Continuous Evaluation: The system continually evaluates model performance to adapt to changing operational patterns or newly labeled anomalies.
• Deployment: AWS IoT SiteWise deploys trained models in your environment for real-time inference on incoming data streams, ensuring high availability and scalability without operational overhead.
• Results Integration: Anomaly detection results are integrated back into AWS, allowing organizations to make informed decisions based on the data.

To learn more about onboarding new hires effectively, check out this excellent resource on the topic. Also, if you’re interested in engaging with pop culture, you might find this blog post insightful. Lastly, it’s essential to consider the benefits of including family in your workplace environment, as highlighted here.

Location: 6401 E HOWDY WELLS AVE, LAS VEGAS NV 89115, Amazon IXD – VGT2