AI Solutions: Practical Machine Learning Before the LLM Wave
MLOps, predictive models, and intelligent automation in the year before ChatGPT changed the conversation.
In 2022, enterprise AI was mostly about practical machine learning — not Hollywood robots. Teams deployed models for forecasting, document classification, anomaly detection, and recommendation — embedded inside existing workflows rather than standalone science projects.
MLOps and responsible delivery
Model training was only half the story. MLOps practices — versioned datasets, reproducible pipelines, monitoring for drift, and rollback paths — separated pilots from production. Azure ML, AWS SageMaker, and open-source stacks (MLflow, Kubeflow) supported end-to-end lifecycles.
Use cases that worked
Invoice extraction, support ticket routing, demand planning, and quality inspection on production lines delivered measurable ROI. Natural language processing existed (BERT-era models), but large-language-model chat was not yet the default interface — that shift arrived later in the year with broader awareness of GPT-class systems.
Ethics and GDPR
EU organisations scrutinised training data, purpose limitation, and human oversight. Explainability and audit trails mattered in regulated sectors.
PrequaliQ focuses on AI that attaches to real business processes — with clear metrics, maintainable pipelines, and governance appropriate to your industry.