--- title: "Machine learning & data" description: "Data pipelines, forecasting and machine-learning features scoped around measurable decisions, with evaluation and monitoring after deployment." canonical: "https://juhenza.com/solutions/machine-learning-development" last-updated: 2026-10-15 --- # Machine learning & data > Data pipelines, forecasting and machine-learning features scoped around measurable decisions, with evaluation and monitoring after deployment. ## Decisions informed by your own data. Useful machine learning starts with a decision worth improving, not a model in search of a use. We build data pipelines, forecasting and ML features scoped around measurable outcomes, with evaluation and monitoring after deployment. ## Fit For teams sitting on operational data they cannot act on, and products that need a prediction, classification or recommendation feature. ## Scope - **Data pipelines & preparation:** Reliable collection, cleaning and structuring of your operational data, with quality checks — because model quality is bounded by data quality. - **Model selection & evaluation:** Baseline-first modelling: simple methods are tried before complex ones, and every candidate is measured against agreed success criteria on held-out data. - **ML features in products:** Prediction, classification and recommendation features served through dependable APIs, with confidence handling and fallbacks when the model is uncertain. - **Monitoring & retraining:** Drift detection, performance tracking and a retraining plan agreed before deployment, so accuracy does not silently decay. ## Process 1. **Define the decision:** Agree the decision the model informs, the cost of errors and what improvement is worth building for. 2. **Baseline, then improve:** Establish the simplest credible baseline and measure; only add complexity when it demonstrably pays. 3. **Deploy with a feedback loop:** Monitor live performance, capture corrections and agree when retraining or stopping is the right response. ## Examples ### Demand forecasting - Challenge: Purchasing and staffing decisions run on gut feel while historical records sit unused. - Approach: Consolidate the history, establish a baseline forecast, then evaluate whether a learned model beats it by enough to matter operationally. - Deliverable: A forecast pipeline with measured accuracy, a review dashboard and honest limitations. ### A classification feature - Challenge: Staff manually categorise documents, tickets or records — slow, inconsistent and expensive. - Approach: Build labelled examples from past decisions, evaluate candidate models against them and deploy with human review on low-confidence cases. - Deliverable: An assisted-classification workflow with measured accuracy and review rules. ### A data foundation - Challenge: Data is scattered across systems with no single reliable source for reporting or analysis. - Approach: Build extraction, validation and consolidation pipelines with documented ownership, then deliver reporting the business trusts. - Deliverable: A validated data pipeline with documented lineage and agreed reporting views. ## FAQ ### Is our data good enough? A short data review answers this honestly: coverage, quality, history and labelling. If the data cannot support the goal yet, building the pipeline to fix that is the project — not a failed model. ### Do you train custom models or use existing services? Whichever the evaluation supports. Established models and APIs often win; custom work earns its cost when the task is specific enough. The comparison is part of the work, not a foregone conclusion. ### How is this different from your AI & automation service? AI & automation focuses on language-heavy work — document intelligence, assistants, agents. Machine learning & data focuses on prediction and analysis over structured operational data. Many projects use both. ## Reference architecture A reference ML workflow. Data quality, honest baselines and post-deployment monitoring decide whether the feature deserves to exist. ### Data Pipelines & validation → Labelling & lineage Deliver training and evaluation data whose quality, coverage and origin are known and repeatable. Review: Check missing values, leakage between train and evaluation sets, and whether the pipeline can be re-run. ### Modelling Baseline first → Evaluation against criteria Compare simple and complex candidates on held-out data against the agreed success criteria. Review: Record error rates by segment, the cost of each error type and the margin over the baseline. ### Serving Prediction API → Monitoring & retraining Serve predictions with confidence handling, and detect drift before users do. Review: Test fallback behaviour, monitor live accuracy against the evaluation plan and agree the retraining trigger. Illustrative planning model; scope and deliverables are agreed per project. ## Technology choices Baselines before complexity ### Data Pipelines · Validation rules · Lineage records Make training and evaluation data repeatable, with quality checks at ingestion. ### Modelling Baseline models · Evaluation sets · Established libraries Compare candidates on held-out data against agreed criteria; add complexity only when it demonstrably pays. ### Serving Prediction API · Confidence thresholds · Drift monitoring Serve predictions with fallbacks and monitor live performance against the evaluation plan. ## Quality checks before handover ### Honest evaluation Check for train/evaluation leakage, report error rates by segment and compare against the baseline. ### Failure behaviour Test what happens on out-of-range inputs and low-confidence predictions; agree human-review rules. ### Live accuracy Monitor predictions against outcomes after release and agree the retraining or rollback trigger. ## Preparing for a project Bring sample data, the decision you want to improve, current error rates if known, and any existing reports or spreadsheets in use. 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