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    Regulated Data Platform & ML Pipeline

    HIPAA-aligned analytics and model delivery for a national care network

    Faster insights without trading off privacy controls
    Client
    Confidential — healthcare
    Industry
    Healthcare & Life Sciences
    Timeline
    7 months
    Technologies
    6+ tools

    The Challenge

    !

    Siloed patient and operational data slowing cohort analysis

    !

    Strict BAAs and regional residency requirements for model training data

    !

    Manual handoffs between data science and platform teams

    !

    Need for lineage, access reviews, and reproducible training runs

    Our Solution

    Built a lakehouse-style platform with encryption, KMS, and network isolation by tier

    Implemented MLOps with experiment tracking, model registry, and promotion gates

    Automated de-identification pipelines with validation checks and sampling

    Integrated RBAC with existing IdP and break-glass procedures for analysts

    Documented control matrix for internal compliance and external audits

    Measurable Impact

    Time to insight

    Faster

    Approved analysts could run governed queries without bespoke extracts

    Model delivery

    More repeatable

    Promotions from staging to production followed a checklist and audit trail

    Risk posture

    Improved

    Fewer ad-hoc copies of sensitive data outside approved stores

    Collaboration

    Smoother

    Clinical, data, and security stakeholders shared a single roadmap

    "We needed science velocity inside a regulated box. The platform team delivered both—without cutting corners on patient privacy."
    C
    Chief data officer
    CDO, healthcare network (NDA)

    Technology stack

    Databricks
    Azure Health Data Services
    MLflow
    Kubernetes
    Terraform
    Apache Airflow

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    Regulated Data Platform & ML Pipeline | Case Study | SystimaNX