Governance

This playbook distills a pragmatic MLOps path on Databricks: from data prep to robust deployment with guardrails. Why another MLOps guide? Focus on operational reality: lineage, reproducibility, cost/latency, and KPI reliability. Re-usable patterns you can drop into teams without heavy ceremony. Reference architecture flowchart TD A["Ingest: Batch/Streaming"] --> B["Bronze Delta"] B --> C["Curate: Features"] C --> D["`ML Training MLflow tracking`"] D --> E["`Registry Stages: Staging/Prod`"] E --> F["Serving/Batch Scoring"] F --> G["`Monitoring Drift, KPI, Cost`"] Building blocks Delta Lake: schema evolution, Z-order, OPTIMIZE + VACUUM policies. MLflow: experiment tracking, model registry, stage transitions with approvals. CI/CD: notebooks/jobs packaged via repo; tests for data contracts and model code. Observability: input DQ, feature coverage, drift monitors, KPI windows, cost budgets. Sample: register and deploy import mlflow from mlflow.tracking import MlflowClient run_id = mlflow.active_run().info.run_id mlflow.sklearn.log_model(model, "model") client = MlflowClient() model_uri = f"runs:/{run_id}/model" client.create_registered_model("churn_model") client.create_model_version("churn_model", model_uri, run_id) client.transition_model_version_stage("churn_model", 1, stage="Staging") Guardrails Promotion requires DQ + performance gates; auto-revert on KPI regression. Cost envelopes by job cluster policy; latency SLOs per endpoint. Takeaways Ship small, measurable increments; automate checks; keep lineage and docs close to the code.

This post shares a practical architecture for privacy-aware data processing on Spark/Delta with PII discovery, redaction, and auditability. It reflects patterns used to replace external DLP APIs with in‑house pipelines. 1) Objectives Discover PII across semi/unstructured data (text/JSON). Redact or tokenize with policy-driven transformations. Preserve utility for analytics and ensure auditability. 2) Architecture Overview flowchart TB A[Landing / Bronze] --> B{PII Scanner} B -->|classified| C[Redaction / Tokenization] C --> D[Delta Curated] B -->|no PII| D D --> E[Access Zones / Views] E --> F[Analytics / BI] D --> G[Audit Tables] 3) PII Detection (Presidio + Spark NLP) Use Presidio analyzers for entities (EMAIL, PHONE, CREDIT_CARD, PERSON, etc.). Complement with domain regex and Spark NLP for names/locations if needed. Confidence thresholds and context words to reduce false positives. from presidio_analyzer import AnalyzerEngine from presidio_anonymizer import AnonymizerEngine analyzer = AnalyzerEngine() anonymizer = AnonymizerEngine() text = "Contact John at john.doe@example.com or +1-555-123-4567" results = analyzer.analyze(text=text, language='en') redacted = anonymizer.anonymize(text=text, anonymize_entities=[{"type": r.entity_type, "new_value": "<REDACTED>"} for r in results]).text print(redacted) 4) Spark Integration Pattern UDF wrapper calling Presidio per row (for small/medium texts); for large docs, batch per partition. Structured outputs: keep original column, plus redacted_text and pii_entities (array of structs). Deterministic tokenization for referential integrity: hash with salt for IDs. from pyspark.sql import functions as F, types as T def redact_text(text: str) -> str: # call analyzer/anonymizer; return redacted text return text # placeholder redact_udf = F.udf(redact_text, T.StringType()) df_out = ( df_in .withColumn("redacted_text", redact_udf(F.col("text"))) ) 5) Governance & Audit Write detections to pii_detection_log with columns: id, entity_type, start, end, confidence, doc_id, ds. Track policy decisions (mask/tokenize/pass) and version the policy set. Access via views: analysts see redacted columns by default; elevated roles can request reveal with approvals. 6) Quality & Monitoring Metrics: percent docs with PII, entity counts by type, redaction coverage. Drift detection on entity distributions to catch model/pattern degradation. Sampling UI for manual review (Streamlit or simple web). 7) Operational Considerations Throughput: consider broadcasted dictionaries/context words; avoid heavy Python UDFs where possible (Scala UDFs or native libs faster). Cost: cache pre-processing, process incrementally, skip re-redaction on unchanged docs (idempotence). Security: store salts/keys in secret scopes; lock down raw zones. 8) Minimal Policy Example version: 0.3.0 entities: - type: EMAIL action: redact - type: PHONE_NUMBER action: redact - type: PERSON action: tokenize token_salt_secret: keyvault://pii-tokens/person-salt 9) Rollout Plan Pilot on one high‑value dataset (support tickets or transcripts). Add governance hooks (policy version table, audit writes, views). Expand coverage by domain; tune thresholds; add sampling UI. Pragmatic takeaway: privacy-aware pipelines protect users and your org while keeping data useful. Bake in policy, audit, and performance from day one.

This post outlines a platform architecture designed to model the impact of a hybrid risk registry (qualitative and quantitative risks) on an oil company’s key financial KPIs like EBITDA and Cash Flow on a monthly basis. The design emphasizes modularity, auditability, and the integration of expert judgment with stochastic simulation. 1. Core Principles & Objectives Single Source of Truth: Establish a centralized, versioned Risk Registry for all identified risks. Hybrid Modeling: Natively support both quantitative risks (modeled with probability distributions) and qualitative risks (modeled with structured expert judgment). Financial Integration: Directly link risk events to a baseline financial plan (P&L, Cash Flow statement) to quantify impact. Probabilistic Output: Move beyond single-point estimates to deliver a distribution of potential outcomes (e.g., P10/P50/P90 EBITDA). Auditability & Reproducibility: Ensure every simulation run is traceable to a specific version of the risk registry, assumptions, and financial baseline. User-Centric Workflow: Provide intuitive interfaces for risk owners to provide input without needing to be simulation experts. 2. High-Level Architecture The platform is designed as a set of modular services that interact through well-defined APIs and a shared data layer. ...

This post captures the practical architecture (2015–2018) that supported the upstream evaluation & stochastic modeling framework outlined in the related post: Upstream Asset Evaluation Framework. It maps legacy design choices to modern terminology and highlights constraints that shaped modeling workflows. 1. Core Principles On‑prem / hybrid HPC + Hadoop (YARN) cluster for heavy simulation; limited early cloud (select AWS EC2/EMR/S3; occasional Azure VM/Blob). No unified “lakehouse” yet: layered zones → Raw (HDFS/S3) → Curated (Hive/Parquet/ORC) → Marts (Hive/Impala/Presto). Limited containers/Kubernetes; batch schedulers dominated (Oozie, early Airflow pilot, Control‑M, Cron). Governance largely manual: Hive Metastore + ad hoc catalog (Excel / SharePoint / SQL). 2. Data Ingestion Source Type Examples Mechanism Notes Geoscience LAS, SEG-Y Batch file drop + ETL parse Large binary + metadata extraction Well / Ops WITSML feeds Batch pull / scheduled parse Standardization step into Hive ERP / Finance CSV / RDBMS exports Sqoop (RDBMS→HDFS), SSIS, Python/.NET ETL Controlled nightly cadence SCADA / Events Downtime logs Kafka 0.8/0.9 (where deployed) or Flume/Logstash Early streaming footprint Market / Pricing Excel price decks Staged in SQL then approved to config tables Manual approval workflow Workflow orchestration: Oozie XML workflows early; selective Airflow DAGs (late 2017–2018) for transparency and dependency visualization. ...

This post reconstructs the evaluation, financial modeling, and decision analytics framework used when leading an upstream (oil & gas) analytics team (circa 2015–2018). It blends technical reservoir & production modeling with fiscal, stochastic, real‑options, and portfolio layers plus emerging carbon governance. 1. Checklist (Top-Level Components) Scope definition Technical (subsurface & production) models Commercial & fiscal models Market & price modeling Cost & economic models Real options layer Stochastic engine & correlations Portfolio aggregation Risk & sensitivity Carbon / ESG integration Data architecture & governance Validation & model risk management Implementation blueprint 2. Scope & Objectives Asset lifecycle: exploration → appraisal → development planning → execution → ramp-up → plateau → decline → abandonment. Decisions supported: license bidding, sanction (FID), phasing, drilling sequence, facility sizing, hedging, M&A, divestment, suspension, expansion, abandonment timing. Outputs: NPV (pre/post tax), IRR, payback, PI, EMV / ENPV, free cash flow profiles, value at risk (P10/P50/P90), option-adjusted value, carbon-adjusted value, capital efficiency, portfolio efficient frontier. ...