MLOps Explained: How to Keep a Machine Learning Model Accurate After Go-Live
What is MLOps and why does a live model need it? Learn how to version, release, monitor for drift and retrain ML models, and use our maturity table.
9 min read
Custom AI Model Training
What Is Custom AI Model Training, and When Do You Need It?
Custom AI model training means training or fine-tuning a model on your own records, documents and images, so it learns the part numbers, defect types and industry terms that off-the-shelf models miss. We audit and label your data, train the model, measure its accuracy on a held-out test set and deliver it to run on your own servers.
Off-the-shelf models don't know your products. A general-purpose model looks impressive in a demo, then stumbles on the things that make your business yours: part numbers, defect types, acceptance criteria, the shorthand your teams use every day. The result is a tool whose every output still needs a person to check it.
The fix is a model trained on your own records, documents and images. We handle the whole job: auditing whether your data is good enough, cleaning and labelling it, choosing and fine-tuning a suitable model, measuring accuracy on data the model has never seen, and wiring it into the software you already run. The model can live on your own servers, so training data does not have to leave the organisation.
From Raw Data to a Working Model
Data Audit & Cleansing
We review ERP records, archived documents, sensor logs or site photos, remove incomplete, duplicate and contradictory entries, and agree exactly what the model will learn from.
Labelling & Dataset Design
Our team labels the data, with joint sessions alongside your specialists where domain knowledge matters. Training, validation and test sets are kept strictly separate.
Model Selection & Fine-Tuning
We pick an image, text or time-series model to suit the problem. Fine-tuning an existing model usually needs far less data and budget than training from scratch.
Measured Accuracy, Agreed Thresholds
Results are reported with precision, recall and a confusion matrix. Together we set the confidence level below which the model hands the decision to a person.
Your Servers or an Edge Device
Run the model in the cloud, on your own servers or on a device on the shop floor. Sensitive data stays in-house and there is no dependency on an outside service.
Monitoring & Retraining
As products, suppliers or processes change, performance is tracked. When it slips, the model is retrained on fresh data and the new version is tested against the old one.
What We Train Models For
- Defect detection and quality grading in production
- Reading documents and forms specific to your sector
- Matching product codes across different sources
- Routing incoming customer requests by topic
- Assistants that use your industry terms correctly
- Demand and sales forecasting
- Terminology and compliance checks on call recordings
- Failure prediction from sensor data
For applied examples, see our computer vision, document OCR and demand forecasting services.
How a Project Runs
01
Feasibility & Data Audit
We assess the volume and quality of your data and tell you plainly whether the problem is worth solving with AI.
02
Dataset Preparation
Data is gathered, cleansed and labelled. Where it is thin, augmentation or synthetic data is considered.
03
Training & Measurement
The model is trained and tuned, then scored on a held-out test set that played no part in training. You see the numbers.
04
Integration & Handover
The model connects to your existing software via API, and your team receives the retraining procedure and documentation.
Not Every Problem Needs AI
If feasibility shows your data is too thin, or that a simple rule set would do the job, we will tell you. We would rather not take budget for a model that won't perform; if needed, we help you build the data collection first.
Questions we hear most often
Frequently Asked Questions
What is a custom AI model?
A custom AI model is trained or fine-tuned on your own records, documents and images. It learns the part numbers, defect types and industry terms that off-the-shelf models miss, so it makes fewer mistakes on the exceptions that are specific to your business, and you no longer need someone to check every output.
How much data do you need to train an AI model?
It depends on the problem, which is why every project starts with a data feasibility review. We assess how much data you have and how good it is, and remove incomplete or contradictory records. If data is scarce, augmentation or synthetic data can help. If the data is not good enough, or a simple rule set would do the job, we tell you plainly.
Should we train from scratch or fine-tune an existing model?
In most cases, fine-tuning an existing model gets results with less data and lower cost than training from scratch. We choose an image, text or time-series model to suit the problem. The right approach is settled during the feasibility stage, based on your data and the accuracy you need.
How is model accuracy measured?
Training, validation and test sets are kept separate. Results are reported on a test set the model never saw during training, using precision, recall and a confusion matrix. Together we agree the confidence level below which the model hands the decision to a person, so it does not guess on uncertain cases.
Can the trained model run on our own servers?
Yes. The model can run in the cloud, on your own servers or on a device on site. Training data and sensitive data can stay in-house, with no dependency on an outside service. The model connects to your existing software through an API, and we hand over the retraining procedure and documentation.
Do AI models go out of date?
Performance can drop as your product range, suppliers or processes change. We monitor it, retrain the model on new data when performance falls, and compare the old and new versions on the same test, so a new version only goes live once it is shown to be better.
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