MLOps | 機器學習作業

Short for machine learning operations, MLOps extend the practices of DevOps (software development IT operations) and ModelOps (model operations) to the nascent field of artificial intelligence (AI), using innovative smart practices to deploy machine learning or deep learning more effectively and reliably. Rather than just developing the ML model, MLOps establish a pipeline or life cycle for the model, through which it can be continuously trained, integrated, and delivered based on feedback from the user. In this way, the ML model becomes increasingly more optimized and better suited for the user scenario it was designed for.

As a solution that's provided to the end user, MLOps encompass a wide range of services, all of which can help the client effectively deploy machine learning in their business. The MLOps solution may include only the software; the software bundled with the hardware; the training that's required to use the solution; or even remote operation of the solution for the client. The three leading cloud solution providers (CSP)—Amazon, Google, and Microsoft—also offer MLOps services on their public clouds. A smaller MLOps vendor can work with their software and hardware instead of its own.

As for the end users who benefit from MLOps, the possibilities are endless. Whether you work in manufacturing, logistics, transportation, commerce, retail, education or healthcare, having an optimized machine learning model will not only improve your own efficiency, but possibly generate additional value from the sea of accumulated data you have in your possession. For this reason, more and more companies and institutions are hiring MLOps services to develop and deploy their own ML models.

GIGABYTE offers both hardware and software for MLOps solutions providers and users alike.

Hardware: GIGABYTE's G-Series GPU Servers, which house multiple graphics cards in a dense and compact chassis to present scalable HPC solutions for AI and deep learning, are the go-to server products for MLOps providers. The GPU servers' parallel computing capabilities are ideal for designing ML models that utilize cutting-edge technologies such as machine vision, handwriting recognition (HWR), and natural language processing (NLP). 

Software: An important facet of MLOps is deep learning, which can be done through deep neural network training. The Myelintek MLSteam DNN Training System, part of GIGABYTE's DNN Training Appliance, is a turnkey DNN training platform that features a preloaded, verified, and optimized hardware and software environment, which includes several common frameworks and libraries. The back end of this MLOps solution is also GIGABYTE's G-Series GPU Servers, as they boast one of the densest configurations of GPUs on the market.