Account

GenAI Platform Engineering

L5-L6 · 8 courses · 113 chapters

Build internal GenAI developer platforms with self-service capabilities, multi-tenancy, RBAC, CI/CD for model/prompt/guardrail pipelines.

What you'll learn

Core responsibilities this discipline prepares you for.

Build the internal GenAI platform

enabling developers to deploy LLM applications self-service

Design platform APIs with golden path templates and self-service provisioning workflows
Build developer portals with pre-approved LLM configurations, guardrails, and monitoring included
Wire end-to-end self-service: from app registration to deployed inference endpoint with observability

Design multi-tenant infrastructure

with namespace isolation and RBAC

Implement Kubernetes namespace isolation with RBAC policies and resource quotas per tenant
Automate tenant provisioning with network policies and admission controllers
Validate tenant isolation by enforcing resource limits under concurrent multi-team workloads

Implement CI/CD pipelines

with GitOps for GenAI applications

Set up ArgoCD GitOps for declarative deployment from Git push to production rollout
Build GitHub Actions workflows with act for local CI and Helm chart packaging
Wire complete GitOps pipelines with Kustomize overlays for dev/staging/production environments

Manage data infrastructure

— databases, caches, message queues on K8s

Deploy PostgreSQL + pgvector, Redis, Kafka, Neo4j, and MinIO as Kubernetes-native services
Configure backup/restore, horizontal scaling, and monitoring for each data component
Benchmark throughput and failover behavior for each infrastructure component under load

Build autoscaling for GenAI workloads

using event-driven scaling and batch job queuing

Configure KEDA for event-driven pod autoscaling based on queue depth, HTTP rate, and custom metrics
Set up Kueue for Kubernetes-native batch job scheduling with priorities and fair quotas
Validate auto-scaling policies under burst GenAI workloads with realistic traffic patterns

Provision infrastructure-as-code

using K8s-native tooling

Declare infrastructure as Kubernetes custom resources with Crossplane providers
Manage databases, storage, and networking declaratively through kubectl apply
Verify reconciliation behavior by modifying infrastructure state and observing self-healing

Implement full-stack observability

across the GenAI platform

Build unified observability with Prometheus metrics, Grafana dashboards, and OpenTelemetry tracing
Add Logfire for Python application tracing and Langfuse for LLM-specific cost and quality monitoring
Wire a unified observability stack spanning infrastructure, application, and LLM inference layers

Operate LLM gateways

as platform infrastructure

Manage LiteLLM gateway operations: API key lifecycle, per-team cost tracking, and provider health
Handle model version migration and zero-downtime provider switching
Operate a production gateway serving multiple internal teams with isolated quotas and routing

Your learning path

8 courses · sequenced for compounding · 113 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Intermediate10 Ch

Step 5

Data Infrastructure Essentials for GenAI

Kafka, pgvector, object stores, and data pipelines — the storage spine under every production GenAI system.

Intermediate10 Ch

Step 6

DevOps Foundations for GenAI Engineers

CI/CD, GitOps, observability — the DevOps practices that make GenAI deployments reliable and reproducible.

Advanced10 Ch

Step 7

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

Advanced21 Ch

Capstone

AI Developer Platform Engineering

Build internal developer platforms for GenAI teams — golden paths, self-service tooling, platform APIs.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

Helm

Package your own GenAI platform charts

Kustomize

Overlay multi-env configs in labs

ArgoCD

GitOps pipeline you set up in a lab

GitHub Actions

CI pipelines you author in labs

Terraform

Provision lab infra as code

FastAPI

Build the control-plane API in labs

PostgreSQL

Metadata store for your platform

Redis

Rate-limit and cache layer

Kafka

Event stream for workload scheduling

Prometheus

Metrics exporter you instrument

Grafana

Platform dashboards you build

LiteLLM

Deploy a multi-provider gateway in labs

Start the GenAI Platform Engineering discipline today

7-day money-back guarantee

Subscribe — $27/mo (6-month plan) →Or save with a 4-pack bundle →