CASE STUDY · DATA ENGINEERING & MLOPS SERVICES
Building an MLOps pipeline that cut model deployment time by 70%.
- 70%Deployment time down from 10 days to 3 days (70% faster)
- 4→12 aReleases from 4 to 12 a month
- 30 minutesRollback possible in under 30 minutes
01 · PROJECT OVERVIEW
The client and the brief.
An anonymised mid-market B2B services company. Name withheld under confidentiality.
Automated model CI/CD with staged promotion, a versioned model registry, data-quality checks and drift alerts. Team: a solution architect, two engineers and a QA specialist for 8 weeks.
- 1Models took about 10 days to reach production because deployments were manual and handed between teams.
- 2Data scientists finished a model and then waited.
- 3An engineer repackaged it, another team provisioned an environment, and someone ran checks by hand from a document.
- 4Each handoff added delay and room for error.
- 5Nobody was sure which model version was live, and a bad release meant a long, stressful scramble to go back.
02 · WHAT WENT WRONG
What went wrong, and how we fixed it.
Every engagement hits problems. These were the ones that mattered here, and what we changed so they did not come back.
- PROBLEM 01In the second sprint, the first model through the pipeline passed every automated check and still behaved badly in staging
The training environment and the serving environment used different versions of a preprocessing library, and a categorical field was encoded differently. We found it by comparing sample predictions between the two. The fix was to pin one shared environment definition for both, and add a parity test that scores a fixed sample in both places and fails if the results differ.
- PROBLEM 02The issue cost about three days and removed a whole class of problem
- PROBLEM 03A second, smaller hurdle was approval
The old process relied on a senior engineer signing off by email, and the new pipeline had no equivalent step. Some people felt exposed. We kept one human approval for production promotion, but made it informed: the approval screen shows evaluation results against the baseline, data-quality status and the list of changes since the last release.
- PROBLEM 04Approvers now decide in minutes with real evidence
03 · THE PROCESS
How we ran it.
We worked in two-week sprints, four in total. Sprint one covered discovery and the target design: we followed two recent releases from end to end and timed every handoff. Sprint two built the registry and the automated path to staging. Sprint three added production promotion, data-quality gates and rollback. Sprint four added drift monitoring, documentation and training. We met the client's data science lead three times a week, ran a demo at each sprint end, and took one real model through the new path each sprint rather than testing on dummy examples.
- 01Step 1
We worked in two-week sprints, four in total.
- 02Step 2
Sprint one covered discovery and the target design: we followed two recent releases from end to end and timed every handoff.
- 03Step 3
Sprint two built the registry and the automated path to staging.
- 04Step 4
Sprint three added production promotion, data-quality gates and rollback.
- 05Step 5
Sprint four added drift monitoring, documentation and training.
- 06Step 6
We met the client's data science lead three times a week, ran a demo at each sprint end, and took one real model through the new path each sprint rather than testing on dummy examples.
04 · RESULTS
What changed.
Measured against the way the work ran before the engagement began.
- NOW 01
Data scientists release models themselves through the pipeline.
- NOW 02
Every model in production has a registry entry and a named owner.
- NOW 03
Releases are small and frequent instead of rare and large.
- NOW 04
A drift alert is treated as a normal operational signal.
- 70%
Deployment time down from 10 days to 3 days (70% faster)
- 4→12 a
Releases from 4 to 12 a month
- 30 minutes
Rollback possible in under 30 minutes
05 · WHAT WE BUILT
How it works.
- Registry: every model is stored with its code version, training data reference, parameters and evaluation results, so any prediction can be traced to a specific model.
- Pipeline: a commit triggers training or packaging, automated tests, evaluation against a baseline, and promotion to staging.Promotion to production needs the checks to pass and one approval.
- Data-quality checks: schema, null rate and range checks on training and serving inputs.A failed check stops the release and tells the owner why.
- Staged promotion: staging first, then a limited share of traffic, then full release.
- Rollback: the previous model stays deployed and warm.Switching back is a configuration change.
- Drift alerts: input and output distributions are compared with a reference window, with alerts to the model owner.
06 · QUICK ANSWERS
The short version.
- What problem did the client have?
- Models took about 10 days to reach production because deployments were manual and handed between teams. Data scientists finished a model and then waited. An engineer repackaged it, another team provisioned an environment, and someone ran checks by hand from a document. Each handoff added delay and room for error. Nobody was sure which model version was live, and a bad release meant a long, stressful scramble to go back.
- What did Brandsmashers build?
- Automated model CI/CD with staged promotion, a versioned model registry, data-quality checks and drift alerts. Team: a solution architect, two engineers and a QA specialist for 8 weeks.
- How was the work run?
- We worked in two-week sprints, four in total. Sprint one covered discovery and the target design: we followed two recent releases from end to end and timed every handoff. Sprint two built the registry and the automated path to staging. Sprint three added production promotion, data-quality gates and rollback. Sprint four added drift monitoring, documentation and training. We met the client's data science lead three times a week, ran a demo at each sprint end, and took one real model through the new path each sprint rather than testing on dummy examples.
- What were the results?
- Deployment time down from 10 days to 3 days (70% faster); releases from 4 to 12 a month; rollback possible in under 30 minutes.
YOUR TURN
Have a similar requirement?
Tell us what is stuck. We will come back with a plan and matched, pre-vetted engineers.
