To Build or Buy an MLOps Platform?

Data Science

It’s the oldest debate in business (or at least the oldest since Silicon Valley invented software-as-a-service).

“If we need software, should we buy something off the shelf or try to build it ourselves?”

That’s a tricky question. Dozens of variables can sway the ups and downs of buying or building software. The right choice can lead to hockey-stick growth for your bottom line, kudos from your board, and high-fives from grateful employees. The wrong choice can leave you slack-jawed, wondering how you got into such a mess.

The stakes are even higher when the question involves AI and machine learning (ML)—technologies that are transforming the world but changing rapidly as they do it. With AI, new solutions and vendors pop up every day. Making a choice can feel like playing three-dimensional chess.

So, what do you really need to consider when looking for an MLOps platform? What are the pros and cons of buying a platform versus building one? Which approach can deliver the goods for your organization, so you get those high-fives and keep the awkward silences to a minimum?

Let’s find out.

What Is an MLOps Platform?

First, let’s clarify what we mean by “machine learning operations platform.” An MLOps platform is a software tool that gives you end-to-end support for your ML lifecycle. With an MLOps platform like Striveworks, you can build new models, deploy them into production, and—most importantly—maintain them, so they deliver good results over the long haul.

An MLOps platform should have strong functionality for these basic steps in the ML value chain:

Data ingestion, storage, and processing
Data exploration and visualization
Model development and training
Model validation, testing, and evaluation
Model deployment and serving
Model monitoring and observation
Model governance and auditability
Model remediation and retraining

Additionally, an MLOps platform typically needs to make it easy for your team to collaborate on machine learning projects, while also maintaining strict security and compliance protocols.

Why Do I Need an MLOps Platform?

Not every organization needs an MLOps platform. But if you plan to fast-track AI adoption and avoid getting left in the wake of your competitors, yours does.

An MLOps platform is a foundational tool for organizations to use ML effectively. Without one, teams are left to their own devices to hand-code models and email Jupyter Notebooks back and forth. If you’re a small organization, maybe that works. But that process isn’t very efficient, and it certainly doesn’t scale. These clunky processes are part of why so many AI projects fail to deliver any value.

An MLOps platform should provide clear, measurable return on investment in the following areas.

Faster time-to-value: By automating and standardizing the ML workflow, you can reduce the time and effort required to build, test, and deploy ML models.
Higher model quality: By using an MLOps platform to test and validate your models, you can improve their accuracy, reliability, and performance. You can also identify and address issues like drift and model bias.
Better scalability: Following a standardized process in a collaborative workspace lets you handle increasing amounts and varying types of data, as well as the growing demand for insights. An MLOps platform also gives you access to cloud services and architectures that can optimize your costs and workflows, especially when you have a lot of models in production.
Increased efficiency: An MLOps platform enables you to automate low-value and repetitive tasks, freeing up your data scientists and machine learning engineers to focus on more complex and creative activities. It also improves productivity through easier collaboration.
Enhanced security and compliance: MLOps platforms enforce security and compliance that protect your data and models from unauthorized access, tampering, or theft. Platforms also make it easy to ensure that your models meet the ethical and regulatory standards of your industry—especially critical if you work in highly sensitive environments where data handling has strict standards.

OK, I Get It. I Need an MLOps Platform. Can I Just Build One In-House?

If you have the resources available, you can certainly instruct your team to build a custom platform for your MLOps work. Building your own MLOps platform can offer real advantages to your organization.

Customizability: Because you decide which features and capabilities your team should build, you can trust that a homegrown MLOps platform will fulfill your needs. If you have an especially rare or unconventional use case, building a custom solution is only a matter of resources.

Control: If you build your own MLOps platform, you maintain full control over its features, integrations, and security. If you have a problem with your system, you decide when it gets solved without going through any gatekeepers.

Knowledge: Building an MLOps platform from the ground up will give your team an unmatched understanding of how it works—knowledge they can apply as they further develop your MLOps program.

Those Are Great Advantages. So, Why Shouldn’t I Build an MLOps Platform?

It’s true: Any bespoke MLOps platform will be finely tailored to your current operational needs. But building a platform from scratch is no easy feat. There is a huge range of pitfalls to consider before you take on an MLOps platform development project.

Time in development: It will require a huge investment in work hours from expert data scientists and software engineers to create a workable prototype of your MLOps platform, let alone a version ready for general availability. The project needs a series of phases—scoping, designing, development, quality assurance, and more—each of which can take weeks or even months to complete. Not only does such a project take the time of your highly skilled (and highly paid) staff, but it also takes time away from their normal projects—presumably ones that are vital to your organization, such as building your products or supporting your customers.
Unknown obstacles: Few projects ever go off without a hitch. Even experts’ best estimates tend to fall short of what is truly needed to accomplish the job. Scope creep, cost overruns, and project mismanagement are all unforeseen, but they can seriously hinder progress. These obstacles are common in home renovations and infrastructure projects, but they’re just as frequent in custom software projects—driving costs through the roof, delaying deployment, and sometimes leading to project abandonment altogether.
Ongoing maintenance: If the project goes well, an MLOps platform still needs regular maintenance. Software doesn’t exist in a vacuum. It needs someone to perform regular system upgrades, fix bugs, install security patches, and make sure the tool does what you need it to do. When building a custom platform, you need to consider how much attention and budget it will require in years to come. It’s also critical to maintain institutional knowledge about the project, or the whole thing may grind to a halt when a key project contributor retires or moves to a new job.
Moving targets: The field of AI/ML is changing so rapidly that it can be hard to keep up. Even with good insight into your current needs, you’d need a crystal ball to predict how the field will evolve just in the time it takes to develop your platform. ML is an ecosystem. Data providers, tools, and applications are in constant flux. Because homegrown MLOps tools are merely a side project to enable your monetizable work, internal developers are likely to struggle with emerging developments—new data types, new architectures, new models—which threaten to leave an outdated platform in their dust. Conversely, MLOps vendors stay on top of these changes as a core function of their business, so their platforms have the knowledge and preparation to handle things like upcoming versions of YOLO, RoBERTa, or Whisper.
Risk and compliance: It’s one thing to build a platform. It’s another thing to build a platform that complies with industry standards and regulations—especially ones that are prone to change because of the new and evolving field of AI. For example, the White House’s executive order from October 2023 set out new requirements for safe and explainable AI—and that’s only one of several pieces of legislation set to steer development of the technology as it gains more and more prominence in business and society.

That Sounds Challenging. Should I Buy an MLOps Platform, Then?

While there are real advantages to buying a commercial MLOps platform, it’s important to understand the full range of considerations that apply when you’re looking for an off-the-shelf solution.

Applicability: What do you really need in an MLOps platform? How do you plan to use it? Is your organization prepared to handle a new tool and the change management that comes with it? It’s not uncommon for a forward-thinking leader to see the value in a technology without considering how it may fit in to their organization, with all its unique quirks and requirements. Consider the questions below. There’s no right or wrong answer, but you want to understand them before you explore a costly software purchase.
- How advanced is your data team? Do you have one data scientist or several? How well established are their processes?
- How many models do you have in production? How many do you plan on building and deploying over the next couple of years?
- How frequently does your data change? Are you doing high-frequency trading, real-time GEOINT, or other work where time is of the essence? Can you handle the rate of change without an MLOps platform?
- What results are you getting from your current ML projects? Are they effective, or do they need adjustment to start bearing good results?
Compatibility: While a homegrown MLOps platform would likely be tailored to your priorities, a commercial solution may not meet 100% of your criteria. One-size-fits-all technology is designed to satisfy many common capabilities. Is the solution you’re exploring close to what you need? Are any of your requirements more “nice-to-have” than “mission critical”? Can custom development bridge the gap for what you need the platform to do? Is it worth ignoring all of the advantages of a commercial platform to take on the headache of building a wholly new one?
Vendor lock-in: Certain MLOps platform vendors make it difficult to extract your data from their system if you want to switch to a new provider. This is a real problem for organizations and end users alike. With proprietary technology, users can end up forced into staying on a platform with limited functionality and high licensing fees to avoid the pain and cost of switching to another vendor. But Striveworks customers don’t need to worry about vendor lock-in. Our platform uses open standards, so our customers can always access their data and models. Users can even export them in one click.

What Are the Advantages of Buying an MLOps Platform?

If your ML team is sophisticated enough to use an MLOps platform, it pays to get started sooner rather than later. Buying an MLOps platform often makes the most sense for the following reasons.

Speed: Obviously, buying a platform that has already been developed is a much faster process than developing one from scratch—and it gives a much faster time-to-value. You could conceivably build, deploy, and maintain your models on a commercial MLOps platform on the same day you sign a contract. A new model could start returning useful inferences for your business needs in hours. Pre-built integrations speed your progress too, saving you the trouble of configuring wholly new ones yourself. Plus, you don’t have to worry about slowing down due to platform updates and maintenance. System upkeep is the vendor’s job.
Accessibility: You don’t need to become a platform engineer if you buy a commercial MLOps solution. Instead, you can start with MLOps right away, taking advantage of a dedicated team’s expertise in designing and developing a worthwhile, stable solution. Junior engineers whose experience may be limited can still get models into production without knowing how to architect an entire platform. Many platforms, including Striveworks, also include no-code features that empower analysts and other non-PhDs to build and deploy new models in a few clicks.
Scalability: Commercial platforms are meant for heavy use by large numbers of users, which makes them ideally set up for scaling your organization’s MLOps program. While a homegrown system may work in its limited scope, a commercial MLOps platform has been tested and tweaked to behave well in lots of scenarios—even when you have five times as many models crunching 50 times as much data.
Security: Any commercial MLOps platform has to adhere to stringent standards for security and compliance in order for its vendor to stay in business. It’s much easier for a homegrown solution with only a small team maintaining it to overlook a critical security update that leaves your data—or your organization—vulnerable.

What About Cost?

Cost is a complicated factor when it comes to choosing to build or buy an MLOps platform. It may seem like a two-way street, but it’s more like an interchange of freeways looping around one another.

Building an MLOps platform requires more funding up front than buying one does. To build one, organizations need to hire new staff or direct existing employees to the project, secure cloud resources or on-prem servers, and fund the project for months or years before it can start to produce a return on investment—if it ever does. Conversely, any organization can buy a license for an MLOps platform at a much lower up-front cost and, conceivably, get profitable results the same day.

Of course, platform licenses add up. Over time, the steady licensing fees of a commercial platform never go away and could outpace the ultimate cost of building a platform in the first place.

Simple, right? Not quite. Both of these scenarios ignore an important additional cost: the ongoing cost of maintenance. Once a homegrown MLOps platform is built, there are no persistent licensing fees, but the platform still needs to be maintained. At a minimum, that includes cloud services and internal staff attention to support the platform. If you plan to further invest in the platform to scale or adjust its capabilities, the cost of supporting it could soar to much more than equivalent costs for a commercial platform. After all, economies of scale let a vendor maintain its platform more cost-effectively.

There’s also the question of opportunity cost. How much profit would you stand to generate if your data team was putting models into production and monetizing your insights instead of trying to construct a brand-new solution?

Of course, cost isn’t a great metric for comparing your MLOps platform options anyway. The right platform should be able to return exponentially more value for your organization—whether it’s built or bought—rendering the question of cost an afterthought. Instead, it makes more sense to focus on time-to-ROI. The faster you can produce effective models—and the longer you can keep them producing—the faster they can generate value and the faster you can scale.

What Do I Need to Know When Deciding to Build or Buy an MLOps Platform?

In AI, like in any hot field, there is no one right or wrong answer for building or buying a platform. An organization’s satisfaction with its tools depends on many factors: company culture, urgency, risk tolerance, budget, customization needs, and more.

That said, as you evaluate your options for an MLOps platform, here are some useful questions to consider.

How much money and time can you invest in MLOps? How much can you do with your current resources? How much support and guidance do you need from a vendor?
How much flexibility and control do you need over your platform? How much customization do you need?
How do you plan to manage your platform over the long haul? Do you have the skills and resources available to do it? If someone leaves, can your platform still function?
How do you see your needs changing down the road? Will you need more capabilities? More data types? More models? More staff to manage your workflows?
What tradeoffs are you comfortable making? Can you tolerate less security for more control on your end? Can you make do with a general platform that has broad functionality, or do you need a tool tailored for your specific domain? Can you work with a vendor to build out the missing functionality you need?
What’s your contingency plan? If your vendor doesn’t work out or your homegrown project stalls, how can you ensure that your organization still makes headway with AI/ML?

In the end, the most important thing is that your organization is able to use AI to do the things it promises to do: analyze more data faster, unlock new capabilities, and support decision-making that drives your business forward. Building or buying can get you there. But which one will get you there the quickest? Which one will produce ROI the longest? And which one will keep you focused on your team’s mission?

Interested in knowing more about the Striveworks MLOps platform? Contact us to schedule a demo today.