Innovative Devops for Artificial Intelligence

Abstract

Developing Artificial Intelligence is a labor intensive task. It implies both storage and computational resources. In this paper, we present a state-of-the-art service based infrastructure for deploying, managing and serving computational models alongside their respective data-sets and virtual environments. Our architecture uses key-based values to store specific graphs and datasets into memory for fast deployment and model training, furthermore leveraging the need for manual data reduction in the drafting and retraining stages. To develop the platform, we used clustering and orchestration to set up services and containers that allow deployment within seconds. In this article, we cover high performance computing concepts such as swarming, GPU resource management for model implementation in production environments with emphasis on standardized development to reduce integration tasks and performance optimization.

Can Artificial Intelligence Transform DevOps?

“…In addition, AI is useful in seeing indicators that cannot be perceived by humans, and early notification and prediction can help teams to acknowledge and fix the problem prior to it can affect SDLC (software development life cycle) [1]. Ciucu et al [16] support that the integration of AI increases the quality of DevOps processes, and AI and DevOps can be integrated by transforming the processing time of DevOps and increasing the quality. ML can enhance the performance of DevOps by reducing inefficiencies in SDLC and automating recurrent tasks.…”

“…The tasks are completed in less time, which lessens the delivery time of the application and increases the end-user satisfaction. Additionally, Ciucu et al [16] propounded that AI integrated with TensorFlow (TF), Caffe, Apache, and Microsoft Cognitive Toolkit in the past few years. Such DevOps deals with environment setup and database management.…”

A Data Analysis platform for high altitude bio monitoring using DEVOPS principles and time series machine learning

“…Devops is well suited for data ingestion and data processing in real time by employing persistent memory databases and fast data schemas. Each unit can be replicated and then purged from the memory while continuous deliv-ering real time support [1,21,22].…”

Open Source Architecture for Iot Based SCADA System for Smart Home

“…Like all computer networks, security and privacy are primary requirements for IoT too. However, while most of the computer networks have dedicated professional resources to attend their security, the Smart Home is a relatively remote system without dedicated specialized security systems, and with minimal technical knowledge from the house owner [11]. This situation presents several challenges to security and privacy which need to be addressed in order for the Smart Home to be feasible [1], [10].…”