Complete for freeAdvancedBrowser simulator included

Course 23

Production ML, Cloud Deployment, and MLOps

Package, version, deploy, monitor, and recover ML services using local containers and simulations for batch, real-time, registries, pipelines, CI/CD, drift, canaries, costs, and incidents. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines MLOps concept simulations with learner-created service, container, monitoring, and incident files made in freely installable or open tools. The C4T page does not run or deploy services; paid cloud resources are optional.

Before you begin

Requirements & setup for Production ML, Cloud Deployment, and MLOps

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

The always-free pathway combines MLOps concept simulations with learner-created service, container, monitoring, and incident files made in freely installable or open tools. The C4T page does not run or deploy services; paid cloud resources are optional.

$0 required

Setup checklist

0 of 9 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

1. Choose the free learning path
Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.
About 2 min
2. Set up C4T Local MLOps Concept Simulator
Open the concept simulator and read its scenario, assumptions, and limitations.
About 5 minOpen C4T Local MLOps Concept Simulator (opens in a new tab)
3. C4T Local MLOps Concept Simulator: step 2
Make a prediction, adjust the available controls, and compare the result with the explanation.
About 5 min
4. C4T Local MLOps Concept Simulator: step 3
Record what changed and why; repeat with at least one different input.
About 5 min
5. C4T Local MLOps Concept Simulator: step 4
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
About 5 min
6. Set up C4T Local MLOps Concept Simulator guided alternative
Open C4T Local MLOps Concept Simulator.
About 5 minOpen C4T Local MLOps Concept Simulator guided alternative (opens in a new tab)
7. Local container runtime: step 2
Choose the browser-based guided pathway.
About 5 min
8. Local container runtime: step 3
Complete this check: Submit the learner-created file or documented design evidence requested by the assignment; the C4T page does not verify local execution.
About 5 min
9. Run the setup check
C4T Local MLOps Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file. Local container runtime: Submit the learner-created file or documented design evidence requested by the assignment; the C4T page does not verify local execution.
About 5 min

Software

2 items

C4T Local MLOps Concept Simulator

Course need

C4T Local MLOps Concept Simulator

Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

Open the concept simulator and read its scenario, assumptions, and limitations.
Make a prediction, adjust the available controls, and compare the result with the explanation.
Record what changed and why; repeat with at least one different input.
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

Open C4T Local MLOps Concept Simulator

Optional paid alternatives

You do not need these options to complete the course.

Commercial cloud platform

Optional comparison outside the C4T site; it may require an account or incur charges and is never part of the always-free path.

Optional only—never required for course completion.

Local container runtime

Optional learner-managed software for extending project files outside the guided concept simulator. It is not supplied, installed, connected, or run by this site.

Optional

C4T Local MLOps Concept Simulator guided alternative

Free

Use the built-in guided concept lab; native installation is an optional extension. The simulator, supplied traces, and written tables cover the assessed concepts without handling physical parts.

Open C4T Local MLOps Concept Simulator.
Choose the browser-based guided pathway.
Complete this check: Submit the learner-created file or documented design evidence requested by the assignment; the C4T page does not verify local execution.

Open C4T Local MLOps Concept Simulator guided alternative

Hardware

Nothing required

No physical hardware is needed for this course.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

Package batch and real-time model inference behind stable interfaces.
Version models and data and assemble testable training or delivery pipelines.
Use CI/CD, monitoring, drift evidence, canaries, and rollback safely.
Analyze reliability, security, cost, performance, and incident response.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.SQLite practice databaseOriginal schema, synthetic seed rows, constraints, and review queries.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Versioned local inference service

Package a tested service with health checks, immutable model identity, and batch or real-time rationale.

Monitoring and deployment game day

Interpret latency, failures, and drift, then execute a simulated canary decision, rollback, and incident review.

Final assessment

Production-style ML application

Deploy locally and monitor a packaged, container-compatible inference service with versioned model, automated workflow, health checks, logs, latency and failure monitoring, drift plan, rollback, cost and performance analysis, and security documentation. Deliverables: Packaged local inference service, Versioned model and automated checks, Health, logging, latency, and failure evidence, Drift, canary, and rollback plans, and Cost, performance, and security analysis.

Prerequisites & pacing

Skills assumed

Backend APIs, containers or command-line basics, and rigorous ML evaluation

Recommended prior courses

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

self paced: 12 weeks at 6 hours per week
weekly: 12 weeks at 6 hours per week

Part of these learning paths

🔬 Research and Production AI · step 2 of 2
Take models out of the notebook — into reproducible science and into monitored production systems.
🛠️ Agentic Systems · step 3 of 5
Build tool-using AI agents that are auditable, permission-bounded, and evaluated against adversaries.

Course created by Zafir Shamsi.