MLOps Engineering: The Complete Course

Team topologies: platform vs embedded ML engineering

Packaging and project structure for ML repositories

Testing ML code: unit, property, and data tests

Deterministic seeds and environment capture

Config-driven training with structured configuration

Batch vs streaming ingestion and ETL/ELT for ML

Data quality checks and data contracts

Data versioning fundamentals with DVC

Lineage tracking and reproducing training data

Feature store architecture: offline and online stores

Point-in-time correctness and training-serving skew

Experiment tracking with MLflow

Weights & Biases and experiment governance at scale

Portable model formats: ONNX and TorchScript

Container-based packaging and artifact signing

The MLflow Model Registry and stage transitions

Rollback-by-registry-pointer

CI pipelines for ML repositories

Testing data contracts and model behavior in CI

Automated retraining triggers

Promotion gates and approval workflows

Docker fundamentals for ML: GPU images and dependency layers

Multi-stage builds and image security scanning

Kubernetes primitives for training: Jobs and CronJobs

Autoscaling inference with HPA and KEDA

Airflow DAGs for training pipelines

Kubeflow Pipelines, Prefect, and Dagster

Terraform basics for ML infrastructure

Environment parity and IaC review gates

Managed ML platforms: SageMaker, Vertex AI, and Azure ML

Cost, vendor lock-in, and multi-cloud abstraction patterns

Batch scoring jobs versus online inference

Online serving protocols: REST and gRPC, and request batching

Triton Inference Server and TorchServe

BentoML and KServe: multi-model serving on Kubernetes

Kafka and streaming feature pipelines

Real-time scoring architectures and delivery tradeoffs

Blue-green deployments and online A/B testing

Golden signals and SLOs for ML services

Dashboards for prediction distributions and slice metrics

Data drift versus concept drift

Statistical drift tests: PSI and KS, and automated alerting

Threat modeling ML systems

Access control, tenant isolation, and compliance for ML

GPU cost management and spot/preemptible training

Cost attribution and cost-aware architecture decisions

Prompt and version management, and RAG pipeline operations

LLM evaluation harnesses, latency/cost tradeoffs, and guardrails

On-call runbooks and incident drills for ML services

Postmortems and disaster recovery for models, data, and registries

Explaining predictions with SHAP and LIME

Fairness and bias auditing as a release gate

Quantization, pruning, and knowledge distillation

Industry case studies and MLOps interview preparation

Capstone I: End-to-End MLOps Platform Build

Capstone II: Production Incident Game Day & Technical Defense