Data Governance for AI Workloads: A Strategic Framework скачать в хорошем качестве

Data Governance for AI Workloads: A Strategic Framework

Data Governance for AI Workloads: A Strategic Framework A comprehensive deep dive for engineering leaders implementing governance for AI workloads at production scale. Covers the strategic decision matrix for build vs buy vs hybrid approaches, critical failure patterns that cost enterprises millions, and a concrete 12-month implementation roadmap with technology recommendations. ================ What you will learn: ================ Traditional Governance Failures at AI Scale Manual/batch controls: Failing real-time inference needs Legacy systems: Breaking on massive (billions+ rows) and unstructured (text, image) data Static rules: Missing model drift Table-level permissions: Insufficient for granular AI control Hidden GPU/infrastructure costs: Lacking attribution AI-Ready Governance Architecture Dynamic Access Control (ABAC): Replacing static RBAC Automated PII detection: Real-time compliance policy gates Dataset-to-model lineage: For reproducibility and audit Drift monitoring: KL divergence, PSI Per-model cost attribution: Training, inference Automated storage tiering: Immutable audit trails Strategic Architecture Decisions Build (In-house): For unique compliance and strong engineering teams (12-18 months) Buy (Vendor): For standard workflows and rapid deployment (2-6 months) Hybrid: Vendor core (e.g., Purview) with custom extensions (e.g., ABAC) (6-12 months) Decision Criteria and Hidden Costs Key criteria: Compliance complexity, data scale (100TB+), and team capability Budgeting: Focus on 3-year Total Cost of Ownership (TCO), not year 1 Hidden costs: Change management, integration (30-50% of budget), and ongoing FTEs Production Failure Patterns and Mitigation Access Bottleneck: Mitigate with self-service automation Hidden Costs: Mitigate with model-level cost attribution Compliance Time Bomb: Mitigate with automated PII scanning gates Model Staleness: Mitigate with drift detection and automated retraining Lineage Black Hole: Mitigate with end-to-end tracking and feature stores Cross-Env Leak: Mitigate with environment-aware access controls Implementation Timeline Months 1-2 (Visibility): Cost dashboards, PII scanning, basic lineage Months 3-6 (Core Platform): ABAC, model registry, audit gates Months 7-12 (Automation): Real-time enforcement, drift automation, optimization Success Factors: Executive sponsorship, cross-functional council Technology Stack Recommendations Microsoft: Azure Purview, Azure Policy, Azure ML Multi-cloud: Collibra, Open Policy Agent (OPA), MLflow Cost: Kubecost, native cloud provider tools Monitoring: Great Expectations, statistical tests (KL/PSI), OpenTelemetry ================ Timestamps: ================ [00:00] Introduction & Agenda [00:42] Why Traditional Data Governance Fails for AI [00:49] Challenge 1: The Volume and Velocity Crisis [01:39] Challenge 2: The Data Quality Collapse [02:37] Challenge 3: Access Control Gaps [03:15] Challenge 4: Cost Explosion [03:50] The Four-Pillar Strategic Framework for AI Governance [03:54] Pillar 1: Dynamic Access Control [04:51] Pillar 2: Lineage and Observability [05:38] Pillar 3: Cost Attribution [06:23] Pillar 4: Compliance Automation [07:11] The "Build vs. Buy" Decision [07:17] Approach 1: Build In-House [07:55] Approach 2: Buy a Vendor Solution [08:25] Approach 3: The Hybrid Approach [08:58] Key Decision Criteria (Compliance, Team, Budget) [09:32] Hidden Costs of Implementation [10:09] Six Common Failure Patterns to Avoid [10:19] Pattern 1: The Access Bottleneck [10:42] Pattern 2: Hidden Training Costs [11:03] Pattern 3: Post-Deployment Compliance Crisis [11:22] Pattern 4: Model Staleness [11:43] Pattern 5: The Lineage Black Hole [12:05] Pattern 6: Cross-Environmental Leak [12:26] 12-Month Phased Implementation Plan [12:36] Phase 1 (Months 1-2): Foundation & Visibility [13:18] Phase 2 (Months 3-6): Core Platform & Self-Service [13:57] Phase 3 (Months 7-12): Full Automation & Optimization [14:29] Critical Success Factors [15:16] Common Technology Stacks [15:29] Summary & Conclusion ================ About Me: ================ I'm Mukul Raina, a Senior Software Engineer and Tech Lead at Microsoft, with a Master's in Computer Science from the University of Oxford. On this channel, I create technical deep dives on System Design and ML/AI architectures. #AIGovernance #DataGovernance #EnterpriseAI #BuildVsBuy #MLOps #ProductionML #AzurePurview #DataLineage #CostAttribution #ComplianceAutomation #AIArchitecture #CloudGovernance #MachineLearning #DataEngineering