Artificial intelligence, machine learning, generative AI and practical AI engineering for industrial environments.
A machine-learning model that works during a pilot is not automatically a production system. Industrial MLOps is about versioning data and models, monitoring drift, controlling…
Read →The best industrial AI systems do not try to remove engineers from the decision. They reduce the amount of information an engineer has to process…
Read →Foundation models for time-series data are an emerging direction for industrial analytics. Their promise is broad generalisation; the engineering challenge is making sure the patterns…
Read →Engineering documentation is full of repetitive work that is structured enough for AI agents to assist with, but important enough to require traceability. This makes…
Read →MES already provides production context that raw machine data often lacks. AI becomes more useful when it can combine that context with process signals instead…
Read →The right location for industrial AI is determined by latency, data volume, availability, privacy and operational ownership. There is no universal winner between edge and…
Read →AI introduces new attack surfaces into OT environments: model endpoints, data pipelines, retrieval systems, tool interfaces and credentials. Security starts with understanding what the AI…
Read →Automation engineering is not disappearing because of AI. The role is moving upward into architecture, data, system integration and engineering judgement. The engineers who understand…
Read →AI in pharmaceutical manufacturing has technical potential, but the engineering problem includes validation, data integrity, traceability and controlled change. The most useful projects are designed…
Read →Generative AI can accelerate PLC engineering, but code generation is not the same as machine design. The safest approach is to use AI for drafting,…
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