Advanced MLOps: ModelOps, DataOps & Monitoring
Streamline ML Workflows from Data to Deployment with Advanced MLOps Practices.
Skills you will gain:
Advanced MLOps: ModelOps, DataOps & Monitoring is a comprehensive program designed to bridge the gap between data science and engineering operations. It equips AI/ML professionals with the tools, practices, and infrastructure knowledge necessary to automate and optimize every stage of the ML lifecycle — from data ingestion to deployment, model retraining, and performance monitoring.
The course addresses the practical challenges of managing ML pipelines in production environments, with a strong focus on version control, reproducibility, automation, scalability, governance, and compliance in enterprise AI systems.
Aim:
To provide in-depth expertise in operationalizing machine learning models by mastering ModelOps, DataOps, and Monitoring for scalable, reliable, and production-ready AI systems.
Program Objectives:
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Provide hands-on exposure to modern MLOps tools and platforms
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Instill best practices in ModelOps and DataOps pipelines
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Enhance skills in monitoring and maintaining ML systems post-deployment
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Empower participants to manage ML lifecycle at scale in the cloud
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Foster deployment-readiness for enterprise AI systems
What you will learn?
🗓️ Week 1: Foundations & DataOps Practices
Module 1: MLOps Evolution & Architecture
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Chapter 1.1: From DevOps to MLOps: How Models Shift the Stack
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Chapter 1.2: Core Pillars: ModelOps, DataOps, ML Observability
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Chapter 1.3: MLOps Tools Landscape: MLflow, TFX, Kubeflow
Module 2: Implementing DataOps for ML
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Chapter 2.1: Data Versioning with DVC & LakeFS
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Chapter 2.2: Data Validation and Schema Testing
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Chapter 2.3: Feature Store Setup: Feast & Vertex AI
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Chapter 2.4: Data Lineage Tracking for Audits
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Chapter 2.5: Automating Data Quality Monitoring
🗓️ Week 2: ModelOps – Deployment, Drift & Feedback
Module 3: Model Lifecycle Management
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Chapter 3.1: Model Packaging: Docker, BentoML
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Chapter 3.2: Deployment Strategies: Canary, Shadow, A/B
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Chapter 3.3: Model Registry: Versioning, Approval, Governance
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Chapter 3.4: Serving Models with FastAPI, Seldon, and KServe
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Chapter 3.5: Feedback Loop Architecture and Data Capture
Module 4: ML Drift & Retraining Strategy
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Chapter 4.1: Concept vs. Data Drift – Definitions and Detection
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Chapter 4.2: Metric Monitoring – Accuracy, Latency, Bias
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Chapter 4.3: Triggering Retraining Pipelines Automatically
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Chapter 4.4: CI/CD for ML – GitHub Actions, Argo Workflows
🗓️ Week 3: Monitoring, Observability & Responsible AI Ops
Module 5: ML Observability & Incident Response
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Chapter 5.1: Setting Up Model Monitoring (Evidently, Prometheus)
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Chapter 5.2: Alerting & RCA in Production Pipelines
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Chapter 5.3: ML Logging & Tracing: OpenTelemetry, ML metadata
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Chapter 5.4: Monitoring Multi-Model Systems
Module 6: Governance, Ethics & Scaling Ops
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Chapter 6.1: Explainability Tools: SHAP, LIME, Captum
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Chapter 6.2: Privacy Preservation & Differential Privacy
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Chapter 6.3: Policy-as-Code & Compliance-as-Code
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Chapter 6.4: Organizational Maturity & Cross-Functional MLOps Teams
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Chapter 6.5: Industry Case Studies: Banking, Healthcare, Retail
Intended For :
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ML Engineers and Data Scientists
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DevOps & Data Engineers transitioning to MLOps
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Software Engineers building ML-enabled systems
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AI/ML Researchers in applied domains
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Cloud Engineers and Platform Architects
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Final-year students and postgraduates in AI, ML, or Data Engineering
Career Supporting Skills