Machine Learning Engineering.
Models that keep working after the data scientist leaves.
Drema builds and operates machine learning systems: predictive models, recommendation engines, forecasting, anomaly detection and computer vision. Our focus is the unglamorous majority of the work — feature pipelines, training reproducibility, drift monitoring and retraining — because a model that cannot be retrained is a depreciating asset.
Models that keep working after the data scientist leaves.
What usually
goes wrong.
A notebook with 94% accuracy is not a machine learning system. The common failure is a model that works on the day it was built, degrades quietly as the world changes, and cannot be retrained because nobody can reproduce the feature pipeline that fed it.
Everything that ships
with this work.
Not a menu to choose from. Each piece is here because leaving it out is what makes this kind of project fail six months later.
Data audit and feature engineering
Assessing whether your data can support the prediction you want, then building the features that make it possible.
Model development
Training, tuning and comparing candidates against a holdout set, with the tradeoffs made explicit.
Reproducible training pipeline
Versioned data, code and parameters so any model can be rebuilt exactly, months later.
Serving infrastructure
Batch or real-time inference with latency and throughput matched to how the prediction is consumed.
Drift and performance monitoring
Alerting when input distributions shift or accuracy decays, before the business notices.
Retraining loop
An automated path from new labelled data to a validated replacement model.
The order matters more
than the tools.
Most of what separates a project that lands from one that stalls is sequence. This is the order we work in, and why each step comes where it does.
- 01
Data assessment
Before promising a model we check volume, labelling, leakage and class balance. Sometimes the honest answer is that the data cannot support the question.
- 02
Baseline
A deliberately simple model first. It sets the bar any complex approach has to beat to justify its operational cost.
- 03
Iterate on features
Feature quality moves accuracy far more than model architecture in most business problems.
- 04
Productionise
Pipeline, serving, monitoring and the retraining path, all versioned.
- 05
Operate
Watch drift, retrain on a schedule or a trigger, and keep a rollback ready.
Where this gets
put to work.
The situations this service is built for. If one of these sounds like your problem it is worth a conversation — and if none of them do, say so on the call and we will point you at what would actually fit.
Churn and propensity scoring
Ranking which customers are about to leave or ready to buy, so teams act on the right ones.
Demand forecasting
Predicting volume for inventory, staffing and capacity planning.
Recommendation engines
Personalised ranking of products, content or matches based on behaviour.
Anomaly detection
Surfacing fraud, faults or outliers in streams too large to review manually.
Have a use case that is not on this list? That is usually the interesting one.
Chosen to fit,
not to impress.
We pick tools that suit the problem and that your team can maintain after we hand over — never to pad a capability list.
Questions we
get asked.
Straight answers, including the ones that talk you out of work we would otherwise be paid for.
How much data do we need for machine learning?
It depends on the problem far more than on a magic number. Simple tabular classification can work with a few thousand well-labelled examples; image or language tasks usually need more, though pre-trained models cut that requirement sharply. We assess this in the first week and tell you honestly if the data is not there.
What is the difference between AI development and machine learning engineering?
In our work, machine learning engineering means training models on your data for a specific prediction, while AI development more often means orchestrating existing large models around your content. Many projects use both, and the scoping call determines which is the right fit.
Who owns the model you build?
You do. Code, trained weights, pipelines and documentation are yours, in your repositories and your cloud accounts. We do not hold your system hostage as a retention strategy.
What happens when the model gets worse over time?
That is expected, and it is why we ship drift monitoring and a retraining path as part of the build rather than as an upsell. You get alerted when input distributions shift or accuracy decays, and a validated replacement can be promoted with a rollback available.
Can you improve a model we already have?
Yes. A common engagement is taking an existing notebook-stage model and making it reproducible, observable and retrainable, which usually delivers more business value than chasing another point of accuracy.
Do we need a data scientist on staff afterwards?
Not necessarily. If the retraining loop is automated and monitored, an engineering team can operate it. We hand over documentation and run a walkthrough so the system is not dependent on us.
Talk it through with a founder.
Bring the actual problem. You will get a straight answer on whether machine learning engineering is the right approach
— including when it is not.
