Machine Learning Development.
From factory signals to clearer operational decisions.
I develop machine learning features and operational analytics around a measurable business problem. For manufacturers, that can mean understanding machine condition, spotting quality patterns or bringing production, downtime and energy data into one useful view. The work connects data collection, evaluated models and the application people use to act on the results. We start with a focused pilot and define success through operational outcomes, data reliability and the cost of running the system.
Discuss your ML use case→
SYSTEM
Choose one production problem worth solving
Machine learning is useful when patterns in your data can support a better decision. In a factory, that might mean detecting unusual vibration, identifying recurring defect conditions or forecasting demand for a production plan. We start with the cost of one problem and the action your team could take when the system spots it.
The first project can cover one line or one machine family. Basic monitoring, a threshold alert or a conventional report may already solve the issue. We establish that baseline before adding a model.
Connect the records behind the factory floor
We assess available machine signals, inspection results, maintenance logs and production records. Machine, product, batch and shift identifiers give readings context. Timestamps, units and missing values are checked before they are used for training or reporting.
Where appropriate, data can arrive through an approved gateway, MQTT feed or API. Your automation team remains involved in industrial connections. The application begins with monitoring and decision support; machine-control changes require a separately engineered scope.
Build and evaluate the right ML feature
An anomaly detector can highlight unusual behaviour. A predictive maintenance model needs evidence linking machine condition to failures or maintenance outcomes. A quality model may use process signals or inspection images, depending on the problem and available labels.
I compare the model with the agreed baseline and test it on later, unseen data. Results include missed events, false alarms and useful warning time. A high accuracy score alone is not proof that the system will improve operations.
Real-time metrics with visible data freshness
The dashboard can show machine status, current-shift output, downtime, first-pass yield, scrap and energy per good unit. OEE can be included where planned time, cycle standards and quality counts are defined consistently. Each metric has a unit, a reporting window and an identified data source.
We agree what real-time means for the workflow: for example, a seconds-level status update or a one-minute production summary. Last-event time, processing delay and stale-feed indicators remain visible. An offline sensor must never look like a healthy machine simply because its last value was normal.
Turn signals into an operational response
An alert needs an owner, a reason and a next action. The workflow can record acknowledgement, investigation and outcome, then connect that information to maintenance or management software. Repeated alerts can be grouped so one incident does not fill a team’s inbox.
Model scores are presented as predictions, with their limitations. Your team can review the evidence before acting. Outcome records also help us measure whether alerts are useful and whether the system needs adjusting.
Measure the operational and financial result
A pilot compares downtime, rejected parts, energy use and alert workload with a documented baseline. We account for changes in product mix, production hours and demand before attributing an improvement to ML. Estimated savings are separated from finance-confirmed results.
Validation also covers permissions, interrupted feeds and recovery after delayed data. Model monitoring tracks false alerts, missed events and changes in the input data. Retraining, support and ongoing infrastructure costs are agreed as operational responsibilities.
Your manufacturing analytics handover
You receive the agreed data integrations, metric definitions, dashboard, ML feature, evaluation report and source code. The handover explains model versions, access rules, data retention and what happens when a source goes offline.
Hardware, industrial commissioning and specialist process advice are identified separately where needed. We document the chosen deployment and maintenance approach so the system can be operated after the initial build.
Start with one machine, one loss and one decision
Share a sample of the data you already collect, the problem you want to reduce and the people who would respond to an alert. Remove sensitive production details from initial examples. I can then propose a discovery or pilot scope and explain whether monitoring, ML or a combination fits.
Before
we begin.
Can ML track everything in a factory?+
Only what the available data can support. Machine state, output and quality usually come from different systems. We define the metrics and check the sources first; a model cannot recover a process event that was never recorded.
Can the dashboard show real-time production metrics?+
Yes, when the source systems provide sufficiently frequent updates. The scope defines refresh intervals, reporting windows, last-event times and stale-feed behaviour. A spreadsheet uploaded once per day supports historical reporting, not a genuinely live machine status.
Do we need sensors before starting?+
Not always. Existing PLC or gateway feeds, inspection records and maintenance logs may be useful. We assess what is available before recommending additional collection. Any industrial hardware connection is coordinated with your automation team.
Is predictive maintenance possible without failure history?+
Some condition monitoring and anomaly detection may be possible. Predicting a specific failure or remaining life requires suitable evidence and validation. The pilot is explicit about which claim the data can support.
Will this guarantee higher profit?+
No. We measure the agreed operational outcome and running costs. Financial benefit depends on demand, production constraints and how the team acts on the information, so savings estimates are reconciled with your business records.
Tell me what
needs to change.
Tell me the process you want to improve, the data available and the decision a live dashboard or model should support.
Discuss your ML use case