MLOps

Productionising ML from experiment to deployment. DVC, MLflow, CI/CD for ML, model serving, drift detection, Kubernetes for ML, and building a mature ML platform.

FundamentalsTopics 1–10

·What is MLOps?
·The ML Lifecycle
·Experiment Tracking
·Data Versioning
·Model Registry
·Virtual Environments
·Reproducibility
·Training Run Logging
·Model Artifacts
·Basic CI/CD for ML

Start Fundamentals →

IntermediateTopics 1–10

·Feature Stores
·Data Pipelines
·Model Serving
·Docker for ML
·Hyperparameter Tuning
·A/B Testing Models
·Evaluation Frameworks
·Metadata & Lineage
·CI/CD for Pipelines
·Testing ML Code

Start Intermediate →

AdvancedTopics 1–10

·Kubernetes for ML
·KubeFlow & Argo
·Distributed Training
·Compression & Quantisation
·Drift Detection
·Real-Time Engineering
·Online Learning
·Shadow Deployment
·Canary Releases
·Multi-Model Serving

Start Advanced →

ProductionTopics 1–10

·ML Platform Architecture
·SLAs for ML Systems
·Incident Response
·Retraining Pipelines
·Monitoring Stack
·Cost Optimisation
·Governance & Compliance
·Model Cards & Documentation
·Multi-Team Platforms
·On-Call Playbooks

Start Production →