Advanced MLOps: ModelOps, DataOps & Monitoring

🗓️ Week 1: Foundations & DataOps Practices
Module 1: MLOps Evolution & Architecture

• Chapter 1.1: From DevOps to MLOps: How Models Shift the Stack

• Chapter 1.2: Core Pillars: ModelOps, DataOps, ML Observability

• Chapter 1.3: MLOps Tools Landscape: MLflow, TFX, Kubeflow

Module 2: Implementing DataOps for ML

• Chapter 2.1: Data Versioning with DVC & LakeFS

• Chapter 2.2: Data Validation and Schema Testing

• Chapter 2.3: Feature Store Setup: Feast & Vertex AI

• Chapter 2.4: Data Lineage Tracking for Audits

• Chapter 2.5: Automating Data Quality Monitoring

🗓️ Week 2: ModelOps – Deployment, Drift & Feedback
Module 3: Model Lifecycle Management

• Chapter 3.1: Model Packaging: Docker, BentoML

• Chapter 3.2: Deployment Strategies: Canary, Shadow, A/B

• Chapter 3.3: Model Registry: Versioning, Approval, Governance

• Chapter 3.4: Serving Models with FastAPI, Seldon, and KServe

• Chapter 3.5: Feedback Loop Architecture and Data Capture

Module 4: ML Drift & Retraining Strategy

• Chapter 4.1: Concept vs. Data Drift – Definitions and Detection

• Chapter 4.2: Metric Monitoring – Accuracy, Latency, Bias

• Chapter 4.3: Triggering Retraining Pipelines Automatically

• Chapter 4.4: CI/CD for ML – GitHub Actions, Argo Workflows

🗓️ Week 3: Monitoring, Observability & Responsible AI Ops
Module 5: ML Observability & Incident Response

• Chapter 5.1: Setting Up Model Monitoring (Evidently, Prometheus)

• Chapter 5.2: Alerting & RCA in Production Pipelines

• Chapter 5.3: ML Logging & Tracing: OpenTelemetry, ML metadata

• Chapter 5.4: Monitoring Multi-Model Systems

Module 6: Governance, Ethics & Scaling Ops

• Chapter 6.1: Explainability Tools: SHAP, LIME, Captum

• Chapter 6.2: Privacy Preservation & Differential Privacy

• Chapter 6.3: Policy-as-Code & Compliance-as-Code

• Chapter 6.4: Organizational Maturity & Cross-Functional MLOps Teams

• Chapter 6.5: Industry Case Studies: Banking, Healthcare, Retail

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