The notebook is not the hard part
By the time most teams ask for ML engineering help, the modelling question is largely answered. A data scientist has a notebook that loads a dataset, builds features, trains a model and shows a good offline metric. What does not exist is everything around it: a pipeline that produces the same features from live data every day, a training run anyone can reproduce, a way for the application to get predictions, and some signal that tells you when the model has quietly stopped being right.
That surrounding system is ordinary software engineering applied to a component that changes with its data. It is where I spend this engagement.
What the work involves
The work follows the path the data takes:
- Ingestion and validation. Pulling from the real sources, not the exported CSV the prototype used, with checks on schema, volume and value ranges that fail loudly rather than training on bad data.
- Feature code. Moving feature logic out of notebook cells into a tested Python package, so training and serving compute features the same way. Training/serving skew is one of the most common reasons a model that looked good offline disappoints in production.
- Training and evaluation. A job that records the data version, parameters and metrics for each run, and compares a new model against the current one before it can be promoted.
- Serving. Often a scheduled batch job writing predictions to a table is simpler and more robust than an online endpoint. Where the product needs live predictions, a service with timeouts, a fallback and a load test.
- Monitoring. Input data quality, prediction distributions, service health, and measured accuracy once outcomes are known.
I have done this kind of work before, including the data pipeline and content recommendation engine for Momspresso and a Word2Vec-based recommendation system for Lenskart, and as Head of AI and Platform at Orangewood Labs, where models had to run on edge hardware.
The signature deliverable
You receive production Python, ML pipelines and model serving: the code in your repositories, the pipeline running in your environment, and a runbook. Illustrative example of a pipeline acceptance checklist:
| Check | Pass condition | Owner |
|---|---|---|
| Daily pipeline runs unattended | Seven consecutive scheduled runs complete or alert | Data engineering |
| Input validation | A deliberately malformed batch is rejected with a clear alert | Data engineering |
| Reproducible training | Re-running a recorded training job reproduces metrics within an agreed tolerance | Data science |
| Promotion gate | A candidate model below the current model’s evaluation score cannot be promoted | Data science |
| Serving fallback | With the model endpoint stopped, the application degrades to the agreed default | Application team |
| Retraining rehearsal | Your engineer completes a retraining cycle using only the runbook | Named owner |
Illustrative example. Not taken from a client engagement.
How acceptance is judged
Acceptance criteria are agreed at scoping and look like the checklist above: operational checks that the pipeline runs and fails safely, a reproducibility check on training, a promotion gate, and a retraining rehearsal performed by your own engineer. Model quality is judged against the evaluation baseline the data scientist sets, not against a number I promise in advance.
Ownership and handover
The code lives in your repositories from the first commit, follows your review process, and runs on your infrastructure. The data scientist remains the owner of the model; a named engineer becomes the owner of the pipeline and serving. The handover includes the runbook, the monitoring dashboard with alert owners, and a recorded walk-through.
When to choose something else
If you want an engineer inside your team rather than a commissioned outcome, see hire a Python and ML engineer. If the source data itself is unreliable or scattered across systems, start with making data and integrations ready for AI. If the problem is inference capacity, GPU cost or LLM latency, see AI infrastructure, inference and cost optimisation.