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From networking basics to production MLOps — learn to deploy, scale, and monitor ML systems with industry-standard tools like Docker, Kubernetes, and AWS.
Chapters 1-2
TCP/IP, HTTP, DNS, SSH, shell scripting
Chapters 3-4
Dockerfiles, EC2, S3, IAM, SageMaker
Chapters 5-6
GitHub Actions, Kubernetes, Helm, EKS
Chapters 7-8
FastAPI, Triton, Prometheus, Grafana
TCP/IP, HTTP/HTTPS, DNS, ports, SSH, REST APIs, gRPC — the networking essentials every AI engineer needs for deploying and debugging distributed systems.
Linux file system, essential commands, process management, shell scripting, cron jobs, and environment variables for managing ML servers.
Dockerfiles, images, containers, volumes, Docker Compose, GPU support, and container registries — packaging ML models for reproducible deployment.
EC2 GPU instances, S3 for datasets, IAM security, VPC networking, Lambda serverless, SageMaker, and cost optimization for ML workloads.
Git workflows, GitHub Actions, testing, Docker builds in CI, deployment pipelines, secrets management, and ML-specific CI/CD patterns.
Kubernetes pods, deployments, services, autoscaling, load balancing, Helm charts, EKS, and distributed GPU training at scale.
Model serving with FastAPI/Triton, GPU management, experiment tracking, feature stores, data pipelines, and end-to-end MLOps workflows.
Structured logging, Prometheus/Grafana metrics, alerting, distributed tracing, health checks, SLOs, security, and incident response.