Automated Validation Framework in Machine Learning Operations for Consistent Data Processing
  IJCTT-book-cover
 		          
 
© 2024 by IJCTT Journal
Volume-72 Issue-8
Year of Publication : 2024
Authors : Sevinthi Kali Sankar Nagarajan, Rajesh Remala, Krishnamurty Raju Mudunuru, Sandip J. Gami
DOI :  10.14445/22312803/IJCTT-V72I8P123

How to Cite?

Sevinthi Kali Sankar Nagarajan, Rajesh Remala, Krishnamurty Raju Mudunuru, Sandip J. Gami, "Automated Validation Framework in Machine Learning Operations for Consistent Data Processing," International Journal of Computer Trends and Technology, vol. 72, no. 8, pp. 155-163, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P123

Abstract
In the realm of Machine Learning Operations (MLOps), ensuring consistent and reliable data processing is paramount for the success of machine learning models. The complexity of managing diverse data sources and the dynamic nature of data quality necessitates robust validation frameworks to maintain data integrity throughout the machine learning lifecycle. This paper proposes an automated validation framework designed to address these challenges and promote consistency in data processing within MLOps workflows. The framework leverages advanced validation techniques, including data profiling, schema validation, and anomaly detection, to identify and rectify inconsistencies and errors in the data. By automating the validation process, organizations can significantly reduce manual effort and streamline data quality assurance, thereby enhancing the efficiency and effectiveness of MLOps. Key features of the framework include real-time monitoring capabilities, customizable validation rulesets, and integration with existing data pipelines. Through empirical analysis and case studies, we demonstrate the efficacy of the framework in improving data quality, reducing operational latency, and mitigating risks associated with faulty data. Ultimately, the automated validation framework offers a scalable and adaptive solution to the challenges of data processing in MLOps, empowering organizations to unleash the full potential of their machine learning initiatives while ensuring data consistency and reliability. Automated Validation Frameworks in Machine Learning Operations (MLOps) have emerged as essential tools to ensure consistent data processing and maintain data integrity throughout the machine learning lifecycle. The framework incorporates advanced algorithms and techniques to automate the validation of diverse data sources, ensuring consistency, accuracy, and reliability. By leveraging machine learning algorithms and statistical methods, it identifies anomalies, outliers, and discrepancies in the data, allowing for timely remediation and error handling. Key components of the framework include data profiling, anomaly detection, data quality metrics, and automated validation pipelines. These components work in concert to assess the quality and reliability of data, providing insights into potential issues and facilitating informed decision-making. Through empirical evaluations and case studies, we demonstrate the effectiveness and scalability of the Automated Validation Framework in real-world MLOps environments. Results show significant improvements in data quality assurance, reduced manual effort and enhanced operational efficiency. Overall, the Automated Validation Framework represents a critical enabler of operational excellence in MLOps, empowering organizations to confidently deploy machine learning models at scale while maintaining stringent data quality standards. Its adoption promises to streamline data processing workflows, mitigate risks, and unlock the full potential of machine learning initiatives. A novel approach to data ingestion leveraging serverless architecture on Amazon Web Services (AWS). Traditional data ingestion methods often face challenges such as scalability limitations and high operational overhead. In contrast, serverless computing offers a promising solution by abstracting infrastructure management and scaling resources dynamically based on demand. We demonstrate the effectiveness of our approach through experimentation and performance evaluation. Results show significant improvements in scalability, resource utilization, and cost efficiency compared to traditional approaches. Additionally, we discussed the design considerations, implementation details, and best practices for deploying and managing the serverless data ingestion framework on AWS. Overall, our framework provides a robust solution for efficiently ingesting data into cloud environments, offering benefits in terms of scalability, flexibility, and cost-effectiveness. By utilizing serverless architecture, the framework enables automatic scaling and resource provisioning, reducing operational overhead and optimizing costs

Keywords
Automated validation framework, MLOps, Data quality assurance, Data validation, Anomaly detection.

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