We all have a pretty good idea of what makes for "Software Engineering Excellence". It’s a playbook refined over decades: write clean, maintainable code, test everything early and often, ship reliably, keep an eye on things in production, and then do it all over again. It’s a solid foundation.
ML excellence is software engineering excellence, plus data excellence, plus learning-system excellence. It inherits all that great software engineering DNA, but it also has to wrestle with a whole new set of challenges:
- messy, ever-changing data,
- models that slowly forget what they’ve learned (we call it "drift"),
- the inherent fuzziness of statistics,
- and a research landscape that moves at lightning speed.
The Cornerstones of Doing ML Engineering Right
-
Staying Ahead: Proactive Maintenance & Quality
- Don't let "technical debt" pile up – whether it's in your data pipelines, notebooks, or the infrastructure itself. Tackle it early.
- Models and even the prompts we use for them can "drift" over time, becoming less accurate. We need to watch for this and fix it.
- Garbage in, garbage out. Ensuring data is high quality, both where it comes from and as it flows through our systems, is paramount.
-
Building Smart: Systematic Execution & Automation
- If it's repetitive and boring, automate it! Think data validation checks, generating new features, or trying out countless model settings (hyper-parameter sweeps).
- Write everything down. Document your code, what version of a dataset you used, where your features came from, and what happened in each experiment.
- Keep an eye on everything: how fast your models respond (latency), how accurate they are and how much they cost to run. Set up alerts so the right people know when something’s off.
-
Always Learning: Continuous Improvement & Innovation
- The ML world changes fast. Keep up with new algorithms, tools, and even hardware that can make your systems better.
- Don't just build it and forget it. Constantly look for ways to improve your metrics, make things cheaper to run, or get answers faster.
-
Defining Success: Clarity & Precision
- Before you even start, figure out what "good" looks like. This means clear business goals (KPIs) and the technical model metrics that support them.
- Be upfront about what your model can't do. Point out its limitations, where biases might creep in, and any weird edge cases – ideally, before your users stumble upon them.
Where ML Feels Familiar to Software Engineers
A lot of this will sound familiar if you're from a software background:
- Quality is King: We still obsess over tests, code reviews, and smooth CI/CD pipelines.
- Automate Everything: Reproducible builds and one-click deployments.
- Know What's Happening: Logs, metrics, alerts, and service level objectives (SLOs) are crucial.
- Small Steps, Big Progress: We work in small batches, stay agile, and use things like feature flags.
- If It's Not Written Down, It Didn't Happen: READMEs, decision records and design docs are essential.
But Here's Where ML Charts Its Own Course
While ML builds on software engineering, it introduces unique twists:
| Dimension | Classical Software | ML Engineering |
|---|---|---|
| Source of Truth | Deterministic code | Data + algorithms that learn (stochastic) |
| How Things Break | Bugs, system outages | Bugs, outages, plus data changing unexpectedly, concepts drifting, and bias |
| Testing Focus | Unit/integration tests for code logic | Data validation, statistical tests, comparing offline vs. online (A/B), shadow deployments |
| What You Deploy | A binary or container | Model weights, feature store setup, training code, a snapshot of the training data |
| Lifespan | Code often stays stable once shipped | Model performance naturally degrades; retraining is a normal part of life |
| The Team You Need | Software Engineers + DevOps | Software Engineers + ML Engineers + Data Scientists + Analysts + Domain Experts |
| Fixing Mistakes | Rollbacks to a previous version | Rollbacks, plus quick data filters, staged feature roll-outs, model "gates" |
| The Toolbox | Git, CI, CD | Git and tools like MLflow, Feature Stores, Experiment Trackers |
Let's understand a few of those key differences:
- Data is like a whole other codebase you didn't write. Every time your dataset updates, it's as if code changes. This adds a huge layer of complexity.
- "It works" isn't always a yes/no answer. Because ML deals with probabilities, You need to think in terms of statistical confidence.
- Models get old. Unlike a piece of software that might run unchanged for years, models age. You're signing up for a long-term relationship: retraining them, possibly re-labelling data, and constantly re-evaluating their performance.
- The stakes are higher when decisions are automated by machines. Ethical considerations like fairness and avoiding bias, along with privacy and regulatory compliance, become central, not just afterthoughts.
To excel in ML engineering, you need strong software skills, to manage data carefully, and to guide AI learning effectively. It’s a challenging, rapidly evolving field, but getting it right means building powerful, reliable, and responsible AI.