Why MLOps and DataOps is More Than Tools
Machine learning and analytical strategy requires a good operating model.
Many people assume that operations is pure tech. It’s a very common assumption, with the saturation of MLOps and DataOps tools out there, and the hyperfocus on demonstrating tech skills. For many companies the big problem isn’t they have the tools or knowledge base. They have it. The biggest pain point is they’re unsure how to use it.
Many business challenges come down to operations. Machine learning and data analytics is no exception. This includes how we connect our data strategy with the people who make it happen. Many companies find this tough because their processes are unplanned and random. Without good operations, data science teams can become cost centers without adding value.
No matter what the tech, you need operations rooted in a product first rather than a model or tech first mindset. Build operations around use cases you need to answer, rather than tools and tech. This helps DataOps and MLOps produce value at scale.
The Role of Operations in Business
Business operations is symphony. While vision of the organization is the musical piece. Tools are the individual musicians; each is important and has a role to play. Operations is like the conductor. It marshals resources and acting as a link between the musical piece’s vision and the doers who bring these plans to life.
A good operating model is a strong bridge between strategy and execution. It takes lofty ideas, lofty visions, and turns them into tangible reality. A company may have brilliant strategists, or stellar workers, but without the magic of operations? Those ambitions can remain just that – dreams.
Now, operations aren't a one-size-fits-all deal. They're as unique as a fingerprint. They really shaped by various elements like the tools you use, your industry, your company culture, your goals, and your resources. The true art of excelling in operations lies in recognizing these elements, playing to your strengths and strategically navigating around weaknesses or inefficiencies.
Efficient and cost-effective operations? That's all about knowing these components inside and out. Knowing these allow you to respond flexibly and decisively - to changing economic, business, and project requirements. Tech is but one factor.
This is a crucial factor to remember in MLOps and DataOps.
Operations is More Than Tech
Checking out Linkedin, it's easy to think that MLOps and DataOps are all about tech. It's like the blind man who touches an elephant's trunk and thinks the entire animal is just like a snake. Tech is crucial to making operations run smoothly, but knowing how to use that tech is equally important.
Imagine tech as a set of top-quality paintbrushes. An artist can own the finest brushes around, but without a clear vision or plan, the brushes alone can't produce a masterpiece. The same is true in business, especially with MLOps and DataOps. Tech assists in handling data and developing models. It can streamline your work and boost efficiency.
Understanding your business goals, collaborating with your team, and having a clear strategy are vital. So while tech helps shape your operations, the overall strategic vision dictates the resources needed to execute it.
Leaders need to make sure that vision behind the strategy is clear and understood, so that operations can be effective. It helps make sure that resources and tools get to the right place, and tools act as a force multiplier—rather than a crutch to lean on.
Tech isn't the end-all. Over-emphasizing it can cause you to overlook the bigger picture. Tech is a means to an end in operations— not the end itself. It cannot solve bad operating models or a lack of strategy. Vision needs a blueprint.
We call this guiding principal "doctrine". It's striking the right balance in execution between tech and business needs in ops. It’s the foundation for an effective operating model.
Vision is the Foundation, Doctrine is Key
Clear strategic vision drives precise operational doctrine. This vision pinpoints the final goals of your data and AI projects. Doctrine, on the other hand, gives your team a set of rules to follow, helping turn future goals into present realities.
Think of operational doctrine as your compass. It guides your project's path, helps choose the right tools, and ensures they're used effectively. To use this compass, you need a solid understanding of your project's goals, user needs, unique data challenges, and the requirements for successful model performance and deployment.
So, why is such a doctrine important? Here's a typical scenario. You have top-notch tools and a skilled team. But things aren't going as planned: your results are off, workflows are messy, and your code needs constant fixing. It seems like there's no standard process to guide you.
Things get even more complicated when people are unsure about the project's scope or goals, leading to inefficiencies and teamwork issues. Team members might feel stuck in their roles, struggling to manage data, business tasks, or ML models effectively.
The missing piece of the puzzle? An operational doctrine aligned with the strategic vision. When this doctrine—the execution method—fails to guide resources and best practices effectively, your data science teams might find it challenging to extract maximum value from their tools.
This issue arises when an overemphasis on tools overshadows the importance of a well-defined doctrine.
Final Thoughts
Building an effective operational model that's rooted in a strong strategic vision and operational doctrine requires time, open communication, and trust. Everyone on the team should know their role and understand how their tasks fit into the larger project. This knowledge can motivate people and boost productivity.
For a data science team, it's important to feel supported by leadership and to understand their place within the larger organization. Likewise, leaders need to appreciate the insights the team generates through their analysis.
A well-crafted operational doctrine, coupled with a strategic vision, can facilitate this understanding. It creates clear lines of communication and speeds up decision-making and action.
While technology can significantly increase efficiency, it's not a silver bullet for a poorly structured operational model or a missing strategy. After all, the ultimate goal of machine learning tasks is problem-solving. As data science practitioners, the most effective way to solve business problems is to follow a process that minimizes redoing work, confusion, and complexity.
Thanks for reading! I write frequently about Data Strategy, MLOps, and machine learning in the cloud.