Data and AI
Machine Learning
Models that earn their place in production
What this involves
How we approach machine learning at Oasis Hive.
A model in a notebook is not a solution. We build machine learning that reaches production with clean data, versioned models, monitoring and a real fallback.
Forecasting, classification and anomaly detection built to run reliably in production.
What we do
Use-case identification
Finding the problems where a model genuinely beats the process you have.
Model development
Feature engineering, training, evaluation and honest baseline comparison.
Production deployment
Serving, versioning, rollback and integration into the systems people use.
Monitoring and drift
Performance tracking, data drift alerts and scheduled retraining.
What you end up with
- Models that improve a measured business metric
- Honest comparison against simple baselines
- Monitored predictions with a safe fallback path
- A retraining routine rather than a one-off experiment
Tools and technologies
We choose per project rather than by habit. These are the ones we most often reach for here.
Questions about this service
Delivered properly
One team from scoping through to support
The people who design the system are the people who answer for it in production, so nothing falls between suppliers.
Proof
Where we have done this
How the work runs
Six steps, no surprises
Step 01
Listen and map
We interview the people doing the work, review the systems in play and write down how things actually flow.
Step 02
Design the solution
We propose what to build first, what to leave alone, what it will cost and what we are assuming. Options come with trade-offs.
Step 03
Build in increments
Working software every two weeks, demonstrated against real data. You can redirect early while changes are still cheap.
Step 04
Verify and secure
Automated tests, security review and load checks run throughout, not at the end, so findings are fixed while the context is fresh.
Step 05
Launch with support
We plan the cutover with you, migrate data carefully, train your team and stay close through the first weeks in production.
Step 06
Run and improve
Monitoring, support and a regular review of what to improve next. Maintained systems stay valuable.
Often paired with
Services that work well alongside this one
AI Consulting
Use AI where it actually pays off
Assessment, prototyping and rollout of AI, with the governance that makes it safe.
Data Analytics
Decisions backed by numbers you trust
Data modelling, pipelines and dashboards that give leaders one agreed version of the truth.
Big Data Engineering
Handle the volume without the meltdown
Streaming and batch platforms that ingest at scale and stay affordable and queryable.
Next step
Let us scope machine learning properly
A short discovery gives you a written specification, a schedule and a fixed price. No obligation to continue afterwards.