NeuroPod: AI-Enhanced Containerization for Sc...

NeuroPod: AI-Enhanced Containerization for Scalable Machine Learning Pipelines on AWS

Author Name : Sai Tarun Kaniganti

ABSTRACT Containerization has dramatically altered the software development and deployment paradigm as a fundamentally sound approach to managing applications. The following paper reviews and details Docker and Kubernetes with AWS to improve ML workflows. They also illustrate how containerization technologies can enhance the construction of today’s advanced data science pipelines through repeatability, scalability, and resource parsimony. Further, the paper evaluates ways artificial intelligence (AI), combined with machine learning (ML), can enhance containerized settings regarding wise resource provisioning, anomaly identification, and even proactive autoscaling. Replication of the work in Responsible Data Science, big data architectures, repeatable training of models, selection of better ones through testing, and creation of MLOps process are analyzed. The paper concludes with future directions for progressing containerized ML, such as more accurate scheduling algorithms, enhanced edge computing, and improved security models.