Practice iii of v · Data and ML platforms
Data you can trace. Models you can explain.
Pipelines, warehouses, feature stores and models that turn raw data into decisions you can act on, in real time, with the lineage to trust them and the monitoring to know when they drift.
Built to be retrainedMonitoring, drift checks and lineage ship with the first model.
01 The problem we solve
A model is only as good as the pipeline under it.
The model is rarely the hard part. The data estate beneath it decides whether a decision can be made in time, explained afterwards and trusted next quarter.
Data in too many places
A fragmented estate of systems, exports and spreadsheets, each with its own version of the customer. Every new model starts with weeks of reconciliation.
Nobody can trace a number
When an auditor or a customer asks why a decision was made, the answer has to lead back to source rows and transformations, not to a notebook on someone's laptop.
Models that quietly decay
A model trained once and left alone drifts as the world changes. Without monitoring and a retraining path, nobody notices until the results do.
02 What we deliver
From raw data to decisions, with the record attached.
We extend the platform you already have where it fits, and build new only where it does not. Every layer is versioned, monitored and documented for the team that inherits it.
Real-time and batch pipelines
Ingestion from core systems, events and third-party APIs, with data contracts checked at the door so bad data stops before it spreads.
- Streaming and batch
- Data contracts
- Quality checks
Spark, Airflow, event pipelines
Warehouses and lakehouses
One governed place for analytical data, modelled for the questions the business actually asks and the access rules it has to keep.
- Lakehouse engineering
- Modelling
- Access control
Snowflake, dbt, PostgreSQL
Feature stores and feature engineering
Features defined once and served the same way to training and to live scoring, so a model sees in production what it saw when it learned.
- Feature engineering
- Point-in-time features
- Vector stores
Python, Redis, Pinecone
MLOps
Training, evaluation and deployment as a repeatable pipeline, with drift monitoring, retraining windows and a rollback for every model version.
- Model registry
- Drift monitoring
- Retraining
Python, FastAPI, CI/CD for ML
Lineage, analytics and BI
Every value in a decision traced to its source, and real-time analytics and business intelligence on top of the same governed data.
- Column-level lineage
- Real-time analytics
- Business intelligence
[Lineage and BI tools to confirm]
Reading Fig. 1
Data enters through contracts, is modelled once in the warehouse and served as the same features to training and to live scoring. A drift monitor watches the model and triggers retraining. The lineage line underneath is what lets anyone trace a decision back to its source.
03 How an engagement runs
Start with the estate you have, not a blank page.
The assessment maps what data exists, who owns it and what decision it has to support. The build extends what fits and replaces only what has to go.
Engagement path
Discovery
Weeks 1 to 2Fixed-fee assessment of the data estate, the decisions it serves and the gaps.
Architecture
Weeks 2 to 4Data contracts, the lineage model, feature definitions and the evaluation plan.
Build
Week 4 onwardDedicated senior team, minimum three months, shipping pipelines and models in increments.
Run
OngoingManaged operations: monitoring, drift checks, retraining and incident response.
How your data is treated
Minimised, located where you say, and recorded.
04 · Case study, fintech
Ninety-two percent of late payments, prevented.
A consumer-credit client needed to lift customer scores at scale. Algobain built the data pipeline and the ML scoring engine behind the platform, and the controls around it.
Read the credit platform case study
The brief
A fragmented data estate, rising late payments and a scoring process that could not keep up with growth. Client name withheld under NDA.
What we built
Parsers that extract and normalise credit-bureau data, a model that predicts score changes, a recommendation engine, and payment-history analysis through secure bank connections.
What it runs on
A Python backend on Flask and FastAPI, PostgreSQL for financial data and Redis for caching, with approval gates on every action that touched a customer's account.
05 · Insights
Getting data out of the systems that hold it.
Most data platforms begin as a migration. These pieces cover how to assess and move it safely.
06 Questions
What data and risk teams ask.
- Can you work with the warehouse we already have?
- Usually, yes. We extend what exists where it fits, and recommend a replacement only where the assessment shows it cannot carry the decisions you need. That recommendation comes in writing, with the cost of both options.
- Real time or batch?
- Both, chosen per decision. A credit limit or a fraud check needs an answer in the request; a weekly report does not. The architecture states the latency each decision needs and builds to that.
- What does lineage mean in practice?
- Any value in a decision can be traced back through each transformation to the source rows it came from. That is what lets you answer an auditor, a regulator or a customer without a forensic project.
- How do you know when a model drifts?
- Drift checks run on a schedule against thresholds agreed during architecture. When a threshold is crossed, the monitor raises it, and retraining or re-scoring runs as a gated action a named owner approves.
- Where is our data processed?
- Where you say. Delivery is GDPR-compliant, data is minimised, and a DPA is available on request for EU and UK entities. Our practices are SOC 2-aligned, not certified. See Trust.
- Can you move data out of a legacy system?
- Yes, with lineage and a rollback for every step, as part of our legacy modernization practice. The old system keeps running until the new path is proven.
Related practices
What reads from the platform, and what it runs on.
Which decision should your data make?
Tell us the decision, the data behind it and how fast it has to happen. We will come back with the platform it needs.
- 1Within 48 hoursA senior engineer replies. If we are not the right fit, we say so.
- 2NDA, then a working sessionWe sign first, then map the estate, the decision and the owners.
- 3A written proposalScope and fixed fee for the two-week assessment.