The underrated virtue in MlOps : discipline

In the world of MLOps, tools and frameworks often take the spotlight—technological choices, CI/CD pipelines, model registries, monitoring systems, infrastructure automation. But beneath all the tech lies an underrated and simple truth : discipline. So how does it affect it all ?

1. Version everything

Version control is the key to everything that is done in data science and related operations. Sometimes, it might feel too much, but the truth is you need the mindset. There are three things that need versioning : code, data and models. On the other hand, you don’t systematically need the most advanced tools, but the simple fact of tracking experimental models with a timestamp and an adjacent readme that quickly summarize what and why you did it is a key point that is often a lost practice.

2. Document adequately

Rigorous documentation is crucial for the reliable advancement of scientific projects. What procedures are followed during project handoffs? How is access ensured to promising work from previous periods? While comprehensive online documentation is not always necessary, maintaining a journal of progress is often a very good start.

3. Monitor regularly

Frankly, implementing monitoring isn’t the most glamorous task for a machine learning engineer. However, robust monitoring capabilities are crucial for professional-level performance, enabling post-incident analysis, root cause identification, and system improvements to prevent issues recurrence. Ignoring this aspect relegates one to the realm of amateur practice.

4. Automate for reproductibility

A common misconception is equating automation with complete automation. Ideally, a sophisticated AI product should feature fully automated datasets generation, model training, and performance alerts. However, a pragmatic approach involves initiating automation incrementally using robust software good practices, dedicating the necessary time to ensure eligibility. Rushing initial implementation often hinders long-term progress; therefore, establishing a solid foundation from the outset is crucial.

Why does discipline matter so much in MLOps?

Machine learning systems are complex and dynamic entities subject to continuous evolution through updates to data, models, and metrics. Without rigorous discipline, system degradation rapidly ensues, rendering current functionalities untraceable and leading to operational instability.

High-performing teams prioritize deliberate action over speed, recognizing discipline as fundamental to scalability, reliability, and fostering trust.

While tools are invaluable, their effectiveness hinges on the application of robust discipline.