AIOps: Transforming Management of Large-Scale Distributed Systems (Published)
AIOps (Artificial Intelligence for IT Operations) is transforming how organizations manage increasingly complex distributed systems. As enterprises adopt cloud-native architectures and microservices at scale, traditional monitoring approaches have reached their limits, unable to handle the volume, velocity, and variety of operational data. AIOps addresses these challenges by integrating machine learning and advanced analytics into IT operations, enabling anomaly detection, predictive analytics, automated incident resolution, enhanced root cause analysis, and optimized capacity planning. The evolution from manual operations to AI-augmented approaches demonstrates significant improvements in system reliability, operational efficiency, and cost reduction. Despite compelling benefits, successful implementation requires overcoming challenges in data quality, model training, cultural adaptation, and drift management. Looking forward, AIOps will continue evolving towards deeper development-operations integration, sophisticated self-healing capabilities, and enhanced natural language interfaces – ultimately transforming how organizations deliver reliable digital services in increasingly complex environments.
Keywords: anomaly detection, incident automation, microservices, predictive analytics, self-healing systems
AI-Powered DevOps: Enhancing Cloud Automation with Intelligent Observability (Published)
This article explores the transformative impact of AI-powered observability on cloud operations and DevOps practices. It examines how intelligent monitoring systems are revolutionizing infrastructure management, deployment strategies, and incident response through advanced anomaly detection, predictive resource allocation, and automated remediation workflows. The integration of technologies like OpenTelemetry, Prometheus, and commercial AIOps platforms enables organizations to shift from reactive to proactive operational models, significantly enhancing system reliability and performance. The article analyzes how AI capabilities extend beyond monitoring to enhance continuous integration and deployment pipelines through automated validation and intelligent rollback mechanisms. Through examination of implementation case studies across financial services, SaaS, and healthcare sectors, the research demonstrates tangible benefits in operational efficiency, deployment success rates, and incident management. The article also addresses implementation challenges, including data quality requirements, alert optimization needs, skills gaps, and integration complexities. By combining telemetry data with artificial intelligence, organizations can achieve unprecedented levels of reliability, efficiency, and agility in their cloud operations.
Keywords: Artificial Intelligence, Cloud observability, anomaly detection, continuous deployment, self-healing infrastructure
Leveraging Cloud AI for Real-time Fraud Detection and Prevention in Financial Transactions (Published)
Financial fraud has increasingly become sophisticated, making it imperative for organizations to implement advanced, scalable solutions for real-time detection and prevention. Cloud-based artificial intelligence (AI) offers financial institutions a powerful advantage, enabling them to analyze vast transaction datasets, swiftly detect anomalies, and effectively mitigate fraudulent activities. This paper confidently demonstrates how Amazon Web Services (AWS) serves as a robust AI-driven framework for fraud detection, harnessing the capabilities of machine learning (ML), anomaly detection, and real-time analytics. We will thoroughly examine critical AWS services, including Amazon SageMaker for streamlined model development, Amazon Fraud Detector for utilizing pre-built ML models specifically designed for fraud detection, AWS Lambda for efficient serverless computing, and Amazon Kinesis for seamless real-time data processing. The integration of these services within the financial ecosystem will be explored, alongside a candid discussion of the challenges associated with implementing such advanced technologies. Additionally, we will present compelling strategies and relevant data to showcase the efficacy of AWS AI solutions in combating financial fraud. An insightful analysis of emerging trends and best practices in AI-driven fraud prevention will round out the discussion, providing a comprehensive overview of the future landscape in this critical area.
Keywords: AWS services, Fraud Detection, Prevention, anomaly detection, cloud AI, financial transaction, machine learning
Leveraging ML for Anomaly Detection in Healthcare Data Warehouses (Published)
The rapid emergence of digitalisation leads to unprecedented growth in the generation of the healthcare sector-particularly EHRs and medical equipment data. This extended the way for challenges for integrity in managing data and anomaly detection, including fraudulent transactions, medication errors, and many more system failures. Modern healthcare data poses a challenge to traditional methods of anomaly detection due to high and complex dimensionality. Machine learning provides a strong solution, using algorithms such as Gaussian Mixture Models, One-Class SVM and deep learning algorithms such as Autoencoders, and Recurrent Neural Networks in the detection of anomalies in healthcare data warehouse settings [1]. This study reports how ML can help advance care for patients, enable the validity of the data and reduce costs through real-time monitoring, fraud detection, and early detection of diseases. Applying anomaly detection through ML would most likely bring better operational performance, patient safety, and decision-making in health care for organizations as issues of poor data quality, lack of interpretability of models, and real-time detection would be addressed [2].
Keywords: Operational Efficiency, Patient Safety, anomaly detection, fraud detection healthcare data warehouses, machine learning
Anomalous Note Change Detection of Nknown Monophonic Melodies (Published)
Anomalous note changes in music melodies are a major concern in several automatic music content analysis tasks. When ‘the musically scale un-related notes’ occur in the note sequence, it will provide less pleasant melodies. These ‘uncommon’ notes are the situations which refer as the ‘anomalous notes’ in a particular melody. Identifying and eliminating the effects of these note changes will helpful to enhance the results in automatic melody evaluation, as well as in automatic melody transcription. In order to address the above issue, this paper proposes an approach to detect anomalous notes changes of unknown monophonic melodies. The proposed model is designed with two main phases with the applicable machine learning and signal processing techniques. Within the first phase, melodies are processed to have their pitch estimations. After the pitch estimation, a note event model has employed with the application of Long Short -Term Memory (LSTM) Neural Network for the detection of anomalous note changes. A set of recorded sample melodies are used as the dataset to evaluate the model. The dataset was collected only for the main seven major scales in music. The model was able to detect anomalous note changes with an overall accuracy of 68.2% for the used dataset.
Keywords: anomaly detection, long short term memory (LSTM, note detection, unknown monophonic melodies