Managing projects – IaaS
If you’re looking to create an AI/ML system in your organization, you’ll have to think about it as its own ecosystem that you’ll need to constantly maintain. This is why you see MLOps and AIOps working in conjunction with DevOps teams. Increasingly so, we will start to see managed services and infrastructure-as-a-service (IaaS) offerings coming out more and more. There has been a shift in the industry toward companies such as Determined AI and Google’s AI platform pipeline tools to meet the needs of the market. At the heart of this need is the desire to ease some of the burdens from companies left scratching their heads as they begin to take on the mammoth task of getting started with an AI system.
Just as DevOps teams became popular with at-scale software development, the result of decades of mistakes, we will see something similar with MLOps and AIOps. Developing a solution and putting it into operation are two different key areas that need to work together. This is doubly true for AI/ML systems. The trend now is on IaaS. This is an important concept to understand because companies just approaching AI often don’t have an understanding of the cost, storage, compute power, and investment required to do AI properly, particularly for DL AI projects that require massive amounts of data to train on.
At this point, most companies haven’t been running AI/ML programs for decades and don’t have dedicated teams. Tech companies such as MAANG (Meta, Amazon, Apple, Netflix, Google) are leading the cultural norms with managing AI/ML, but most companies that will need to embrace AI are not in tech and are largely unprepared for the technical debt AI adoption will pose for their engineering teams to manage.
Shortcuts taken to get AI initiatives off the ground will require code refactoring or changing how your data is stored and managed, which is why strategizing and planning for AI adoption is so crucial. This is why so many of these IaaS services are popping up to help keep engineering teams nimble should they require changes in the future as well. The infrastructure needed to keep AI teams up and running is going to change as time goes on, and the advantage of using an IaaS provider is that you can run all your projects and only pay for the time your AI developers are actually using data to train models.
