Class Location

Online class via Zoom

Student's home

Tutor's Home

Years of Experience in Azure Devops Training

12

Class Location

Online class via Zoom

Student's home

Tutor's Home

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in DevOps Training

13

Teaching Experience in detail in DevOps Training

1. Introduction to MLOps What is MLOps? Definitions and objectives Why MLOps is needed (ML lifecycle problems it solves) MLOps vs DevOps vs DataOps MLOps maturity levels and phases Roles in MLOps (ML engineer, MLOps engineer, DevOps) 2. Fundamentals & Environment Setup Linux basics for MLOps (commands, SSH, file system) — optional foundational module Python fundamentals (if needed) IDEs, notebooks, GitHub repositories Setting up Python environments and dependencies 3. Version Control & Experiment Tracking Git essentials (clone, commit, push, branches, pull requests) GitHub/GitLab workflows Experiment tracking tools (MLflow, Weights & Biases) Data versioning with DVC Dataset and model artifact version management 4. Continuous Integration & Continuous Deployment (CI/CD) Fundamentals of CI/CD for machine learning Pipelines for automated testing and model promotion GitHub Actions / GitLab CI for ML workflows Automated retraining triggers Ensuring reproducibility and testing in pipelines 5. Containerization & Orchestration Introduction to Docker and container fundamentals Building Docker images for ML models Container registries and versioning Kubernetes basics (pods, deployments, services) Orchestrating scalable model deployments 6. Model Deployment Serving models (REST API with FastAPI / Flask) Batch vs real-time serving Deploying on cloud platforms (AWS/Azure/GCP) Load balancing, autoscaling, and routing Using platform services (EKS/AKS/GKE) 7. Monitoring & Logging Model performance monitoring in production Detecting data drift and model drift Metrics (latency, accuracy, throughput) Logging with ELK / Prometheus & Grafana Alerts and incident responses 8. Model Governance & Security Compliance and audit trails Access control and secure deployments Ethical AI considerations Metadata tracking and lineage Model registry best practices 9. Cloud & Scalable Infrastructure Core cloud services for MLOps Infrastructure-as-Code (Terraform, Ansible) Managed pipeline tools (Kubeflow, Airflow) Cost optimization strategies 10. Capstone Projects (Hands-On) Pick at least 2 real projects to build a strong portfolio: Project A: End-to-End MLOps Pipeline Ingest data → Train → Register model → Deploy → Monitor Project B: CI/CD for Model Retraining Automate model retraining on new data + test/validate + redeploy Project C: Scaling with Kubernetes Containerize models → Deploy on K8s → Setup metrics dashboard 🎯 Skills You’ll Gain ✔ End-to-end ML lifecycle automation ✔ Git + experiment & data versioning ✔ CI/CD with automated pipelines ✔ Docker & Kubernetes for serving ✔ Model monitoring and performance logging ✔ Cloud deployment best practices ✔ Security, compliance & governance approaches