Within the context of MLOps, the advantages of using a multi-tenant system are manifold. Machine learning engineers, data scientists, analysts, modelers, and other practitioners contributing to MLOps processes often have to perform similar activities with equally similar software stacks. It’s hugely helpful for an organization to take care of only instance of the stack or its capabilities—this cuts costs, saves time, and enhances collaboration. In essence, MLOps teams on multi-tenant systems may be exponentially more efficient because they aren’t wasting time switching between two different stacks or systems. 

Growing demand for multi-tenancy

Adoption of multi-tenant systems is growing, and for good reason. These systems help unify compute environments, discouraging those scenarios where individual groups arrange their very own bespoke systems. Fractured compute environments like these are highly duplicative and exacerbate cost of ownership because each group likely needs a dedicated team to maintain their local system operational. This also results in inconsistency. In a big company, you may have some groups running software that’s on version 7 and others running version 8. You could have groups that use certain pieces of technology but not others. The list goes on. These inconsistencies create an absence of common understanding of what’s happening across the system, which then exposes the potential for risk.

Ultimately, multi-tenancy isn’t a of a platform: It is a baseline security capability. It’s not sufficient to easily plaster on security as an afterthought. It must be an element of a system’s fundamental architecture. Considered one of the best advantages for teams that endeavor to construct multi-tenant systems is the implicit architectural commitment to security, because security is inherent to multi-tenant systems.

Challenges and best practices

Despite the advantages of implementing multi-tenant systems, they don’t come without challenges. Considered one of the major hurdles for these systems, no matter discipline, is scale. Every time any scaling operation kicks off, patterns emerge that likely weren’t apparent before.

As you start to scale, you garner more diverse user experiences and expectations. Suddenly, you end up in a world where users begin to interact with whatever is being scaled and use the tool in ways that you simply hadn’t anticipated. The larger and more fundamental challenge is that  you’ve to find a way to administer more complexity.

If you’re constructing something multi-tenant, you’re likely constructing a typical operating platform that multiple users are going to make use of. That is a vital consideration. Something that’s multi-tenant can be more likely to develop into a fundamental a part of your online business since it’s such a meaningful investment. 

To successfully execute on constructing multi-tenant systems, strong product management is crucial, especially if the system is built by and for machine learning experts. It’s vital that the people designing and constructing a domain-specific system have deep fluency in the sphere, enabling them to work backward from their end users’ requirements and capabilities while with the ability to anticipate future business and technology trends. This need is just underscored in evolving domains like machine learning, as demonstrated by the proliferation and growth of MLOps systems.

Apart from these best practices, ensure to obsessively test each component of the system and the interactions and workflows they allow—we’re talking lots of of times—and convey in users to check each element and emergent property of functionality. Sometimes, you will find that you must implement things in a specific way due to the business or technology. But you actually need to be true to your users and the way they’re using the system to unravel an issue. You never need to misinterpret a user’s needs. A user may come to you and say, “Hey, I would like a faster horse.” Chances are you’ll then spend all of your time training a faster horse, when what they really needed was a more reliable and rapid technique of conveyance that isn’t necessarily powered by hay.