Bay Information Systems

MLE vs SWE

In traditional software engineering, success is often defined by delivery. A feature is scoped, built, tested, and shipped. The outcome is a working system that behaves as expected. Machine learning projects, by contrast, rarely follow this pattern. Success is conditional, behaviour is probabilistic, and iteration is central.

This article outlines three key differences that affect how ML projects are built, managed, and delivered. It is intended for technical stakeholders who are experienced in software but new to ML.

1. Success is Statistical, Not Binary

A software feature is either working or broken. An ML model is usually somewhere in between. Success in ML is measured statistically via agreed metrics and always relative to a baseline.

This changes the language of delivery. You do not ask, “Does it work?”, but rather, “How well does it work, and is that good enough for this context?”

The output is not a deterministic component but a probabilistic function of data. The boundary between acceptable and unacceptable performance is contextual, and often negotiated with the domain team.

2. The product is Behaviour not Code

In ML systems, much of the logic is learned rather than explicitly written. This means:

The training data and labelling choices affect model performance more than code.
A model retrained on fresh data may behave differently, even if the code is unchanged.
Debugging often involves examining data quality, feature drift, or class imbalance, not code paths.

This introduces complexity in testing and validation. Unlike unit tests, which assume fixed outputs, ML tests must track behavioural patterns and distributions over time.

3. Delivery is the Start, Not the End

ML projects do not finish when the model is deployed. They should be expected to improve with time when appropriate feedback loops, fresh data, and retraining mechanisms are in place.

This means effective ML delivery requires:

Continuous monitoring (for performance decay or data drift)
A path for retraining or fine-tuning
Instrumentation to capture feedback or user signals

Project teams must treat delivery as the beginning of an optimisation loop, not its conclusion.

This shift also affects team structure. ML delivery benefits from close collaboration between data engineers, product owners, and operations teams.

4. ML Projects Start with a Waterfall Phase

While ML systems are often iterative at the model and evaluation level, they typically begin with a more structured, waterfall-style phase. This is especially true for teams deploying ML in greenfield environments or where infrastructure is immature.

Early stages often require:

Provisioning data pipelines
Validating data availability and quality
Defining labelling strategies and annotation tools
Deploying infrastructure for training, experimentation, and evaluation

This foundational work is sequential and prerequisites later experimentation. It makes early investment critical and delays the onset of true iteration until the platform is in place.

Practical Takeaways for Cross-Functional Teams

If you’re a PM, tech lead, or engineer managing ML alongside software components, consider the following:

Frame scope around performance targets, not just features. Define acceptable baselines early.
Expect iteration. Initial model versions are usually proof-of-concept, not final systems.
Budget for data work. Cleaning, labelling, and pipeline stability are core parts of the ML system.
Plan for monitoring. Set up infrastructure for detecting failure modes, not just logging errors.
Collaborate closely. ML teams need support from software, infra, and product from day one.

ML vs Software Methodologies

Category	Common Software Methods	Common ML Methods
Sequential Planning	Waterfall	Feasibility Study + Data Audit
Iterative Delivery	Agile	Experiment-Measure-Refine Cycle
Continuous Improvement	DevOps / CI-CD	Model Monitoring + Retraining

Closing

ML projects succeed not when the model is deployed, but when it is delivering consistent value in a live context. This requires different expectations, different rhythms, and a mindset that tolerates uncertainty in service of insight.

As more organisations integrate ML into their product stack, understanding these differences helps cross-functional teams work more effectively.