There are several factors that influence the decision to build vs buy an MLOps platform - read this to decide what's best for you.
Chances are, you’ve likely heard of Machine Learning Operations (MLOps) and how it can help you succeed at generating value with AI. And, if you’re reading this, you’re probably experiencing the telltale signs that you need to invest in an MLOps capability (e.g., overly long production deployment timelines, friction between data scientists and developers, and point solutions that don’t enable composable business and technical architectures.) This led you to where you are now in your solutions research, evaluating whether to build vs. buy an MLOps platform so you can present a clear concept, budget, and timeline to all necessary stakeholders.
There’s obviously a lot more to MLOps than just the tech solutions (e.g., talent, data, processes, infrastructure, governance), but if it’s the technical components that interest you the most, give the previous installment in this series, MLOps Architecture: Building Your MLOps Pipeline, a quick read for a reference MLOps technical stack. There are several enterprise and open-source technologies mapped to the stack that solve for one or more components of the modern AI pipeline – data prep, training/experimentation, deployment, serving, and monitoring. More than likely, your organization has already invested in the first two components and is ready to productionize those investments.
If you’re committed to introducing a modern MLOps capability, finding the right solution boils down to three approaches: build it in- house, buy it, or a hybrid of the two. If you are exploring one of these approaches, you need to consider factors like readiness for scale (both talent and processes), financial resources and total cost of ownership (TCO), the uniqueness of your business model, and your urgency, or time-to-value.
With an MLOps approach, you can significantly reduce the time it takes to move a model into production. With powerful pipelines that can automatically deploy models from nearly any training tool or framework into open-source containers, you’ve turned your models into immutable API endpoints that any software developer can use or integrate into any application. The next step after deciding that MLOps can help your team is determining whether to build vs. buy your MLOps platform, and fortunately there are several different options to explore.
Turning to a commercially ready enterprise or open-source solution is the best option if you need to demonstrate value quickly or have insufficient resources or time to build a fully functioning MLOps capability in-house. This alternative, which is often less expensive than building in-house, will reward you with a faster path to productionizing your AI projects, standards and familiar APIs and SDKs that make integrations easy, and regular feature updates. When considering this approach, you also need to examine a few other factors– whether to pursue enterprise or open-source solutions that offer best-of-breed components or end-to-end capabilities. A strategic assessment of both near- and long-term needs (e.g., company size, expertise, tech flexibility, inference pipeline complexities, risk posture) will help you decide what’s best for your organization. Consider using the AIIA’s reference tech stack to identify and try candidate solutions to assess the speed and ease at which they integrate into your existing workflows, applications, and tech stack so that you reduce adoption friction and technical debt.
For most organizations, the best option is often a hybrid approach– build the components that are core to your business (e.g., data prep and custom models) and outsource the proven functions that help you accelerate time to value. Not only will this save you time, resources, and give you the greatest flexibility, but it will also preserve your organization’s ability to stay focused on what’s core to your business while generating maximum value from your AI investments.