A prototype classifies selected documents in a test folder.
Applied AI · Practical guide
What does an AI prototype need to work in a business?
A demonstration tests a possibility. An operational solution also needs available data, integration, monitoring and a way to respond to failure.
Explore the guideThe central idea
A model needs a system around it.
The model is one part of the system. Its value depends on how it receives information, delivers results and connects with people’s work. Moving into operation starts by checking these conditions, not by adding a screen to a demonstration.
At T2G we start with the task, its data and operating conditions. We agree on a scope that can be checked and explain technical decisions throughout the work.
Step by step
Prepare the solution for real use.
Four points to organise the work. Each diagram supports the explanation; the process adapts to its context.
- 01 / 04
Define how results will be used
Clarify who uses the result, for which decision and within what time. Daily predictions may work in batches, while interactive tasks need different response times. Agree what is acceptable and what happens if no estimate is available.
User · Task · Criterion - 02 / 04
Connect inputs and results
Check formats, permissions, quality and data versions. Define how the model is called, where its response is stored and how it relates to the original case. Test missing, duplicated and unexpected inputs.
Data · Model · System - 03 / 04
Prepare review and recovery
Agree when a person intervenes, how errors are recorded and how to restore a previous version. An automation should be stoppable without halting all operations. A defined manual workflow may provide the fallback.
Result · Review · Fallback - 04 / 04
Monitor and maintain
Record versions, response times, failures and changes in inputs. Review real results when references become available. Changing data does not automatically call for retraining: investigate the cause and evaluate again.
Monitor · Investigate · Update
An explained example
From classifying files to supporting a workflow
Business use needs real file intake, source records, error handling, suggestions shown to reviewers and saved corrections. Each part needs clear ownership.
For your decision
What needs to be clear.
Integration
Inputs and responses connected with the real process.
Operations
Access, records, review and recovery from errors.
Maintenance
Owners and criteria for reviewing or changing the model.
Common questions
Questions before getting started.
Does every model need real-time responses?
No. Batch processing may be sufficient and simpler when the process does not need an immediate answer.
Is constant retraining necessary?
Not just because time has passed. Changes need relevant signals, suitable data and evaluation to justify them.
Further technical reading: Google · Rules of Machine Learning ↗
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