Audit a proof-of-concept model's data pipeline and code against defined production readiness criteria.
Digital Transformation & Artificial Intelligence
Scaling AI Pilots from Proof of Concept to Enterprise Production
Gives data and engineering teams a disciplined path from a working proof of concept to a monitored, owned production AI system, avoiding pilots that stall before scale.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A working notebook is not a production system, yet many organisations discover this only after a pilot has been promised to the business. Proof-of-concept models are usually built for speed, with manual data steps and no monitoring, and that is precisely why so few ever reach reliable enterprise production. This course sets out what scaling an AI pilot actually requires: rebuilding ad hoc pipelines into repeatable, tested workflows, registering and versioning models so a rollback is always possible, and instrumenting live systems so drift and failure are caught before they reach a business decision. Participants audit a pilot against production readiness criteria, design the monitoring and incident response a live model needs, and plan the handover from data science to the engineering and operations teams who will run it day to day. Cost and capacity planning for inference at scale are treated as first-class design decisions, not afterthoughts. Participants leave with a checklist and pipeline design they can apply to their own pilot's route to production.
Expected Learning Outcomes
Rebuild an ad hoc pipeline into a version-controlled, tested workflow suitable for daily operation.
Register and version production models with a documented rollback procedure for each release.
Instrument a live model with monitoring for latency, drift and output quality, with clear alert thresholds.
Forecast and control inference cost as usage grows from a pilot cohort to full production volume.
Write handover documentation and an on-call plan that lets engineering operate a model without its original author.
Set the change control and retraining triggers that govern a model once it is in production.
Who Should Attend
Data scientists whose pilot models are being asked to move into full production use.
Machine learning engineers responsible for building and maintaining deployment pipelines.
Platform and DevOps engineers extending existing CI/CD practice to cover machine learning workloads.
Technical leads accountable for the reliability of AI systems once they reach live users.
Product managers who must set realistic timelines for taking an AI pilot to production.
IT operations staff who will inherit on-call responsibility for a production AI system.
Course Modules
Select any module to see its sessions and points.
01Why AI Pilots Stall Before Reaching Production
2 sessions · 8 points
Session 1Diagnosing the Gap Between a Pilot and a Production System
- Audit a proof-of-concept model's code, data pipeline and dependencies against what a production environment requires.
- Identify manual steps in a pilot, such as hand-copied data or ad hoc scripts, that will not survive daily operation.
- Distinguish a pilot that failed on technical merit from one that failed only because it was never engineered to scale.
- Estimate the true engineering effort to industrialise a pilot before promising a production delivery date.
Session 2Setting Production Readiness Criteria Before Scaling
- Set production readiness criteria covering data pipeline reliability, latency, security review and support ownership.
- Agree the service level a production AI system must meet before the business will depend on its output.
- Require a documented rollback plan as a condition of approval for every model moving into production.
- Confirm that a named team has accepted operational ownership before a pilot is scheduled for scale-up.
02Engineering the Path From Notebook to Pipeline
2 sessions · 8 points
Session 1Building Repeatable Data and Training Pipelines
- Rebuild an ad hoc data preparation script as a repeatable, version-controlled pipeline with automated testing.
- Separate training, validation and inference data flows so a change to one does not silently affect the others.
- Introduce continuous integration checks that catch a broken feature pipeline before it reaches a live model.
- Package model dependencies in a controlled environment so behaviour is consistent between testing and production.
Session 2Establishing Model Registries, Versioning and Rollback
- Register every production model with its version, training data snapshot and performance baseline recorded.
- Run a challenger model against the current production model on live traffic before a full replacement.
- Define the rollback trigger and procedure that returns a prior model version to service within a set time.
- Control access to the model registry so a new version cannot reach production without a recorded approval.
03Operating AI Systems at Production Scale
2 sessions · 8 points
Session 1Monitoring and Incident Response for Live Models
- Instrument production models with monitoring for latency, error rate, input drift and output distribution.
- Set alert thresholds that distinguish a genuine model problem from normal variation in incoming data.
- Write an incident response procedure for a misbehaving model that names the decision-maker for taking it offline.
- Review a sample of production predictions against actual outcomes on a fixed schedule, not only after complaints.
Session 2Managing Inference Cost and Capacity as Usage Grows
- Forecast inference cost against expected usage growth before committing a model to full production traffic.
- Apply batching, caching or model compression to control cost as call volume increases.
- Set a budget owner and cost ceiling for each production model, reviewed alongside its business benefit.
- Right-size compute allocation using observed load patterns rather than the assumptions made during the pilot.
04Organisational Handover and Sustained Ownership
2 sessions · 8 points
Session 1Transferring Ownership From Data Science to Engineering
- Write handover documentation that lets an engineering team operate a model without the original data scientist.
- Define the escalation path between operations and the data science team for issues beyond routine support.
- Confirm on-call coverage and response time for a production model before data science ownership is released.
- Run a joint operating period where both teams share responsibility before full handover is complete.
Session 2Governing Change Control and Retraining in Production
- Set the change control process a retrained or updated model must pass before replacing the version in production.
- Schedule retraining against a defined trigger, such as measured drift, rather than an arbitrary calendar date.
- Record every production model change in a log that links the change to its business and technical justification.
- Review the production AI portfolio periodically to confirm each system still merits its operating cost.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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