Practical, hands-on guides for DevOps engineers stepping into the world of machine learning operations. From ML basics to GPU workloads on Kubernetes.
From ML fundamentals to production Kubernetes — structured learning paths for every stage.
ML concepts, terminology, and the mental model shift from DevOps to MLOps.
2 guides →Dataset pipelines, data prep, model training, and production serving with KServe.
5 guides →K8s native features, GPU scheduling, and running distributed training workloads.
2 guides →Data drift detection, model decay, dataset versioning, and automated retraining.
2 guides →The best places to start your MLOps journey.
Understand the ML workflow, core terminology, and the Python ecosystem — then train your first model.
MLOps PipelineIngestion patterns, data validation, S3 structure, Airflow DAGs, and DVC versioning from scratch.
Kubernetes for MLNVIDIA device plugin, GPU Operator, CUDA runtime, MIG partitioning, and DCGM monitoring.
Follow this sequence to go from zero to production MLOps in ~6 hours.
11 practical guides covering the complete MLOps lifecycle.
ML workflow vs CI/CD, Python ecosystem, and training your first model.
FoundationMental model shift, maturity model, tool mapping, and 90-day plan.
MLOps PipelineIngestion, validation, S3 structure, Airflow DAGs, Argo Workflows.
MLOps PipelineCleaning, feature engineering, train/val/test splits, Feast Feature Store.
MLOps PipelineTraining loop, MLflow tracking, hyperparameter tuning, K8s Jobs.
MLOps PipelineInferenceService YAML, canary deployments, autoscaling, monitoring.
Kubernetes for MLNode affinity, taints/tolerations, PV, Jobs, HPA, resource quotas.
Kubernetes for MLNVIDIA device plugin, GPU Operator, CUDA, MIG, DCGM monitoring.
OperationsDrift detection, Evidently AI, DVC, MLflow aliases, automated retraining.
OperationsDVC pointer files, S3 remote storage, and step-by-step dataset versioning so every model links to the exact data it was trained on.
MLOps PipelineAutomate dataset versioning with Airflow on Kubernetes: DAGs, KubernetesExecutor, Pod Identity, and DVC push to S3.