| dc.description.abstract | In this era of big data, extracting useful information from massive amounts of data has
become a challenge for many enterprises. Therefore, many enterprises have invested in
artificial intelligence technologies (Machine/Deep Learning) to process large amounts of data
using AI computations and bring new value to their businesses. However, the development
process of ML (Machine Learning) models is complex and involves many professionals in
various fields, as well as many environment configurations, which results in the entire ML
development team having to spend a lot of communication costs, which also affects the actual
benefits of the model for the enterprise. In recent years, the concept of MLOps has emerged,
which is DevOps on Machine Learning, aimed at reducing human costs and accelerating the
development life cycle during development. There are now many MLOps platforms that use
containerization technology to package the steps of ML and use container orchestration tools
such as Kubernetes to manage tasks. However, sometimes external resources outside the
cluster need to be used in ML development, and existing platforms do not provide the ability
to integrate external resources. Therefore, this study will design an ML Workflow system
based on FaaS technology, allowing users to customize their ML Workflow through a
workflow platform and encapsulate steps into FaaS. This will deploy internal and external
resources as an event-triggered function that the system can call, deployed on Kubernetes, and
ultimately allow users to create reusable ML Workflows and ML models. | en_US |