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Masterclass

Production AI Masterclass: Observability, Reliability, Scale

Take AI prototypes to production: observability, streaming, reliability, and horizontal scale.

Start this masterclass

Still deciding? Ask first.

Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.

  • Curriculum fit, prerequisites, or where to start
  • Honest answer, no pressure to enroll

Engineers are learning here from

NVIDIAMICROSOFTGRABWISEPIPEDRIVEBOLTGLIA

Outcome

What you'll be able to do.

Own the production layer of your AI stack: observability, reliability, latency, cost, scale.

  • A full observability stack with traces, evals, and regression alerts
  • A layered production architecture with replayable traces and clear failure domains
  • A streaming LLM app with token-level delivery and graceful degradation
  • A RAG system deployed on Kubernetes with Ray for horizontal scale

Projects you build

Portfolio pieces you can demo.

Each project ships as real code you run locally, not slides you watch. Walk into your next review with something on screen.

01Project

An LLM observability dashboard for your team

Every LLM call traced. Cost per feature, per day. Alerts when retrieval quality drops. The view every AI team wants but few actually build.

PhoenixLangfusePostgres

You ship: Screenshots of a real dashboard + the CI config that catches regressions before merge. Staff-engineer portfolio energy.

02Project

A streaming chat that scales

Token-by-token streaming. Backpressure handled. Graceful degradation when a provider slows. Survives a thousand concurrent connections without falling over.

FastAPISSEUvicorn

You ship: A load-tested app that proves it stays up under real concurrency. The difference between a demo and a product.

03Project

A RAG system on Kubernetes with Ray

Deploy a real retrieval pipeline that horizontally scales. Autoscaling workers. Queue depth. The ops story senior AI hiring managers probe for.

KubernetesRayPython

You ship: A deployment diagram and benchmark numbers you can walk a staff-level interviewer through. Few AI engineers can.

Curriculum

What's inside.

  1. 01

    Streaming LLM Applications with FastAPI

    Stream LLM responses in real-time and master prompt engineering fundamentals.

  2. 02

    LLM Observability with Arize Phoenix

    Emit JSON logs with request IDs and OpenTelemetry spans across every tool call so you can click a trace and see exactly what your agent did.

  3. 03

    Multi-agent tracing with OpenTelemetry and Phoenix

    Stop debugging supervisor handoffs by guessing. Trace every agent, every tool, every routing decision.

  4. 04

    Production agentic systems with Langfuse

    Turn a notebook agent into a service you would be happy to wake up to at 3am.

  5. 05

    Layered production AI architecture

    Architect an agent as seven composable layers with per-request traces.

  6. 06

    Enterprise RAG infrastructure with Kubernetes and Ray

    Ship a RAG service that survives real production traffic on Kubernetes.

Who it's for

Is this for you?

AI engineers

whose prototypes keep breaking the moment real users touch them

Backend engineers

owning the production AI layer and tired of flying blind

Platform engineers

scaling RAG and agent systems beyond single-node toy setups

What you'll earn

Ship it, earn it.

Observability Owner

Ship a full AI observability stack

Scale Architect

Deploy AI systems on Kubernetes at real traffic

Pricing

Pick the path that fits.

Self-paced forever, or mentor-led when you want live feedback.

FAQ

Frequently Asked Questions

Who is this for?
Engineers who already ship AI features but keep getting surprised by production: hallucinations nobody caught, cost spikes nobody predicted, latency issues nobody traced. The masterclass teaches the layer that makes AI systems reliable.
Do I need Kubernetes experience?
Helpful but not required. The Kubernetes and Ray sections build up from why horizontal scale matters for RAG and agents, not from zero infrastructure knowledge.
What will I ship?
A full observability stack with traces and evals, a layered AI architecture with replayable failures, a streaming LLM UX, and a horizontally scaled RAG deployment. Each one maps to a production problem engineers actually hit.

Pick your next step.

Take AI prototypes to production: observability, streaming, reliability, and horizontal scale.

Start this masterclass

Production AI Masterclass: Observability, Reliability, Scale

Self-paced masterclass

Enroll