Breaking boundaries: Empowering channel partners to unite DevOps and MLOps for a stronger software supply chain

As businesses incorporate machine learning into their commercial strategies, the race to innovate and stay ahead of the market has highlighted some new challenges.

Traditionally, software development (DevOps) and machine learning (MLOps) teams have operated with separate workflows, tools, and objectives. In today’s environment, this leads to inefficiencies and redundancies that can hamper software delivery.

What are the risks of sticking with the status quo? How can the two teams be bridged together?

Siloed pipelines create blockers

DevOps pipelines are built around continuous integration and delivery (CI/CD), emphasizing speed and reliability. MLOps, on the other hand, introduces stages such as data preparation, training models, and validation. When these operations are managed separately, the handoff from data science to engineering can be slow and error-prone. Data scientists may work in one environment, while engineers work in another, often requiring manual steps that disrupt the overall software lifecycle.

Different toolchains only exacerbate the problem. DevOps and MLOps both require automation, reproducibility, and version control. However, keeping two systems running at the same time is a waste of resources when they’re designed to achieve the same goal.

Channel providers who serve these infrastructures typically have to deal with many of these cases, adding complexity without delivering extra value. Silos between teams further complicate matters, with broken communication and misaligned objectives.

Unlike traditional code, ML models often rely on dynamic, data-driven outputs that can change depending on their training data or hyperparameters used. As a result, they don’t always fit neatly into existing DevOps pipelines, meaning standard testing, validation, or security checks can be skipped or inconsistently applied.

These problems increase the time it takes to get AI-powered features to market. Limited traceability of model versions, training data, and hyperparameters makes troubleshooting and auditing cumbersome, raising concerns about governance, compliance, and accountability.

The case for unification

The solution, as many organizations are discovering, is to combine DevOps and MLOps into a single, cohesive software supply chain. This approach doesn’t overlook the unique requirements of machine learning, though. Indeed, it treats artificial intelligence (AI) as if it were any other software component, creating a system of consistent protocols, whether for code snippets or trained models.

DevOps and MLOps share some of the same goals: rapid delivery, automation, and reliability. Aligning around these goals helps organizations and channel partners to operate more efficiently, reduce redundant work, and foster better collaboration.

The way to achieve true unification is to treat ML models as first-class software artifacts. Like binaries, libraries, and configuration files, models should be versioned, tested, and distributed through the same automated pipelines. This ensures unified visibility, so teams can keep track of which model version aligns with which release, reducing confusion and ensuring reproducibility.

Integrating models into these workflows extends automation across the entire lifecycle, from preparing data to deploying it, which cuts down on human handoffs and speeds up delivery from start to finish.

This approach also improves collaboration between data scientists, engineers, and operations teams. Sharing infrastructure and using the same processes makes communication simpler and allows for smoother handoffs.

Governance is also strengthened by subjecting ML models to the same quality assurance, security scanning, and compliance checks as other software components. For channel partners tasked with safeguarding software supply chains, this consistency is essential.

Opportunities for the channel

For the IT channel, bringing DevOps and MLOps together is both a challenge and an opportunity.

Organizations want to use AI, but they often lack the skills or infrastructure to do so. Partners who help customers set up these pipelines enable them to deliver faster, more reliable, and compliant solutions. Channel providers are at the vanguard of AI-driven innovation when they bridge the gap between DevOps and MLOps.

Companies need to be able to quickly and safely migrate models from testing to production to realize AI’s potential. There has to be a single software supply chain where ML models are handled like first-class assets and workflows are automated from start to finish. For channel partners, this method helps customers grow their AI projects while making sure that quality, security, and governance are maintained throughout the software lifecycle.

As organisations race to adopt more software and models, the industry needs holistic governance. Currently, only 60% of companies have full visibility into software that is running in production. Combining DevOps and MLOps into one software supply chain can help companies achieve their shared goals of rapid delivery, automation, and reliability. This will create an efficient and secure environment for building, testing, and deploying the entire spectrum of software, from application code to machine learning models.