Many companies face challenges with traditional rule-based systems for anomaly detection, resulting in delayed reporting, desensitized engineers, and decreased performance. Addressing these issues is crucial to minimizing the impact of incidents and improving overall operational efficiency.

  • Rule-based systems rely on predefined rules, making them less adaptable to changes in the data patterns.
  • Setting appropriate thresholds for rule-based anomaly detection can be challenging. A threshold that is too low may lead to false positives, while a threshold that is too high may result in false negatives.
  • When the individual data items are large and arrive rapidly and from varied sources, static analysis is not an option. Distribution of input data shifts over time — for example, during a holiday shopping event, or when a new product is launched, phone/internet network is unstable. In such settings, the anomaly thresholds need to be adjusted automatically.
  • Rule-based systems lack the ability to learn and adapt autonomously from the data. They cannot adjust their rules dynamically based on evolving patterns, which limits their ability to respond to changing environments.

Watch Solution Video

The solution offered by Agivant involves comprehensive analysis of incoming tickets, logs, metrics, and traces to proactively identify and address issues within an organization's systems. Using advanced AI/ML techniques, the solution conducts Root Cause Analysis (RCA) and trend analysis to identify recurring patterns and anomalies in real-time.

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Components of the solution

Collection and Preprocessing: Data Gather and preprocess data from various sources.
Establishing a Baseline: Define a baseline of normalcy using historical data.
Algorithm Selection: Choose appropriate algorithms for anomaly detection.
Model Training and Fitting: Train models on historical data and fit them to current data.
Anomaly Detection and Validation: Detect anomalies in real-time and validate them against predefined thresholds.
Feedback Loop: Continuously update models based on feedback and evolving patterns.
Continuous Monitoring: Monitor key metrics in real-time for proactive response.

How do we update our model in real-time?

Brute Force Updates

The simplest solution is to simply recompute our parameters on the most recent data window every time a new data point arrives. However, this can be infeasible if fitting the model to the window is too computationally complex.

Scheduled Updates

We can cache our model parameters for a given period of time, say 24 hours, and retrain on the new data points at the end of each period. However, excessive false positive alerts can occur if the behavior of our metric changes before our scheduled update.

Event Driven Updates

If a high prediction error for the recent set of data points has been detected, we can use this as an opportunity to recompute our model parameters. Event-driven updates are unpredictable, which can lead to operational challenges in the future.

Online Updates

For some algorithms, it is also possible to reformulate them to work in the online setting: continuously reading in new data points and efficiently updating the parameters with each data point.

Key Benefits

Comprehensive Data Analysis

Agivant's solution analyzes various types of data sources including tickets, logs, metrics, and traces.

RCA and Trend Analysis

Through sophisticated AI/ML algorithms, the solution conducts Root Cause Analysis (RCA) and trend analysis to identify recurring trends and potential issues.

Real-time Anomaly Detection

The solution detects anomalies in real-time for both one-off and recurring issues, enabling swift action to mitigate potential disruptions.

Post-call Analysis

Post-call analysis is conducted to identify and address new issues that may arise, ensuring continuous improvement of systems and processes.

Minimization of False Positives

Advanced algorithms are employed to minimize false positives, ensuring that alerts are accurate and actionable.

Rapid Alerting

Alerts are sent to the appropriate teams within minutes of detecting anomalies, enabling timely response and resolution.

Agivant empowers companies to enhance incident response, minimize downtime, and optimize operational efficiency through proactive anomaly detection and monitoring. By leveraging advanced AI/ML techniques, companies can stay ahead of potential issues and maintain high-performance levels in dynamic data environments.

DevOps Engineer

Industry experience: 10 Years
Location: Pune

To apply for this opportunity, write to us at careers@agivant.com with the following subject: AG5111-DevOps Engineer

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The quest for relevance defines the exponential acceleration of humanity. This has presented us with a slew of opportunities, but also many unprecedented challenges. With technology-led innovation, we help our customers harness these opportunities and address the myriad challenges.