PIXELBANKv8.2.1
Menu
Sign In
Infrastructure & Deployment for AI/ML Engineers

Systems & Networking

From networking basics to production MLOps — learn to deploy, scale, and monitor ML systems with industry-standard tools like Docker, Kubernetes, and AWS.

8 Chapters65 TopicsHands-on Walkthroughs

Recommended Study Path

Phase 1

Networking & Linux

Chapters 1-2

  • Ch 1: Networking Fundamentals
  • Ch 2: Linux & Shell

TCP/IP, HTTP, DNS, SSH, shell scripting

Phase 2

Containers & Cloud

Chapters 3-4

  • Ch 3: Docker & Containerization
  • Ch 4: Cloud & AWS Essentials

Dockerfiles, EC2, S3, IAM, SageMaker

Phase 3

CI/CD & Orchestration

Chapters 5-6

  • Ch 5: CI/CD & Pipelines
  • Ch 6: Orchestration & Scaling

GitHub Actions, Kubernetes, Helm, EKS

Phase 4

ML Infra & Monitoring

Chapters 7-8

  • Ch 7: ML Infrastructure
  • Ch 8: Monitoring & Observability

FastAPI, Triton, Prometheus, Grafana

Chapters 1-3 are free. Cloud, CI/CD, K8s, ML Infra, and Monitoring require Pro.
Each topic includes real-world tools and hands-on walkthroughs.

All Chapters

Networking Fundamentals

TCP/IP, HTTP/HTTPS, DNS, ports, SSH, REST APIs, gRPC — the networking essentials every AI engineer needs for deploying and debugging distributed systems.

How the Internet WorksTCP/IP & UDPHTTP & HTTPS+5
Start Learning

Linux & Shell

Linux file system, essential commands, process management, shell scripting, cron jobs, and environment variables for managing ML servers.

Linux File SystemEssential CommandsProcess Management+5
Start Learning

Docker & Containerization

Dockerfiles, images, containers, volumes, Docker Compose, GPU support, and container registries — packaging ML models for reproducible deployment.

Why Containers?Docker BasicsWriting Dockerfiles+5
Start Learning
PRO

Cloud & AWS Essentials

EC2 GPU instances, S3 for datasets, IAM security, VPC networking, Lambda serverless, SageMaker, and cost optimization for ML workloads.

Cloud Computing ConceptsEC2 & ComputeS3 & Object Storage+5
Pro Only
PRO

CI/CD & Pipelines

Git workflows, GitHub Actions, testing, Docker builds in CI, deployment pipelines, secrets management, and ML-specific CI/CD patterns.

Git WorkflowsGitHub Actions BasicsTesting & Linting+5
Pro Only
PRO

Orchestration & Scaling

Kubernetes pods, deployments, services, autoscaling, load balancing, Helm charts, EKS, and distributed GPU training at scale.

Why Orchestration?Kubernetes Core ConceptsDeployments & Services+5
Pro Only
PRO

ML Infrastructure

Model serving with FastAPI/Triton, GPU management, experiment tracking, feature stores, data pipelines, and end-to-end MLOps workflows.

Model Serving OverviewFastAPI for MLTorchServe & Triton+6
Pro Only
PRO

Monitoring & Observability

Structured logging, Prometheus/Grafana metrics, alerting, distributed tracing, health checks, SLOs, security, and incident response.

Logging FundamentalsMetrics & DashboardsAlerting+5
Pro Only

Practice Problem Sets

Sharpen your skills with coding challenges and system design problems.

Where to Go Next

ML Study Plan

From Linear Regression to Deep Learning

CV Study Plan

Computer Vision: Algorithms and Applications