Boosting Resiliency with ML-Based Telemetry Analytics Architecture
Tags: aws, cloud, datascience, database
Published: 2023-09-16 08:09:30

Data is integral to every digital application for driving core functionalities across several modules. Each app collects, stores, and processes data to function correctly, and most of them use specialized hardware (sensors) to record each data metric precisely.

Factual information is recorded from remote sensors using a [multiplexer ](https://en.wikipedia.org/wiki/Multiplexer)for analysis and app improvements. This process falls under telemetry analysis which is crucial for app performance breakdown and maintenance. 

With machine learning, telemetry systems can overcome various data analysis and processing challenges. 

This blog will discuss the influence of machine learning on telemetry analysis and how top players implement it for proactive monitoring.

## ML-Based Telemetry Architecture
Machine learning is pivotal in almost every enterprise application to speed up operations. Organizations can collect, aggregate, detect, and resolve data anomalies with a fully equipped ML-based telemetric architecture, completely transforming application performance. 

Big tech players use ML-based analysis to revolutionize their applications across diverse sectors. The top two renowned data science solution providers are: 

### AWS

Amazon Web Services (AWS) telemetry analytics architecture comes equipped with multiple tools that employ machine learning abilities, maximizing data consumption for desired outcomes. 

AWS [Glue](https://en.wikipedia.org/wiki/AWS_Glue), [Lambda](https://en.wikipedia.org/wiki/AWS_Lambda),  Amazon EMR, API Gateway, and [Kineses ](https://en.wikipedia.org/wiki/Amazon_Kinesis)are some tools that provide diverse data analytic functionality, from real-time data processing/streaming to serverless computing.

### Microsoft Azure

Microsoft Azure is another prominent name in the tech industry with ML-based data analytic and processing tools and development environments. Azure IoT hub, data lake storage, and steam analytics are some essential tools it provides for detailed telemetry analysis. 

## How does ML Help Boost Resiliency?

Machine learning models and data processing rates are the differentiating factors of both enterprise solutions that distinctively utilize the power of AI. Here’s how ML helps organizations get the exact outcomes. 

### Fault Prediction

With the proper use of ML-based telemetry architecture systems, organizations can detect anomalies in data. 

For instance, by monitoring server performance, network bandwidth capacities, and application database requests with the power of AI, they can identify data patterns for fault prediction. 

We can use Amazon CloudWatch Metrics to analyze a specific data metric, tracking anomalies to identify issues that can lead to future app crashes proactively. 

### Incident Response Recovery

Machine learning comes in handy when recovering from server crashes and system failures. ML-based systems can process thousands of API calls at blazing speeds. 

For instance, Amazon API Gateway, part of AWS ML-based telemetry architecture, provides an audit, covering everything from user actions to security and unauthorized behavior by analyzing data patterns with the help of AI. 

### Scalability

ML-based telemetry solutions are game-changers for systems upscaling with time. With proper utilization of AI, businesses can perform analysis of various data metrics as a whole. Data points from remote servers, applications, and users can be analyzed using ML-powered tools. 

For instance, Amazon OpenSearch enables organizations to log diverse data metrics, combining information from business data and telemetry reports.

## Conclusion

This blog covered all crucial aspects of data analytics in the machine learning era, providing an overview of two renowned ML-based telemetry analytics architectures. With AWS’s complete data analytic toolkit, businesses can use the power of AI to extract crucial information from remote data sensors.