Advanced

MLOps & AI Infrastructure

Build the platforms that power AI at scale

Deploy, monitor, and scale ML models in production. CI/CD for ML, Kubernetes, and cloud infrastructure.

Enroll Now — $1,999 See Curriculum
190
Total Hours
12
Weeks
6
Modules
~16
Hrs/Week
Course Components
📘
6
Topics
🔬
2
Hands-on Labs
📋
2
Quizzes
Legend
Topic
Exercise
Lab
Quiz
Mini Project
Capstone
Activity
Reading
Forum
Career
Complete Course Structure & Table of Contents
📡
Live instructor-led delivery · classes run 3 days a week. The topics below are covered in live sessions; recorded versions will be available after class delivery.
MOD 01
ML Deployment Fundamentals
Containerization, APIs, and serving infrastructure
4h 3m ▾
📘
Docker for ML Engineers Topic
Containerizing models, multi-stage builds, GPU Docker
30 min
📘
Model Serving Architectures Topic
REST, gRPC, batch vs real-time, model formats
28 min
📘
Kubernetes for ML Topic
K8s deployments, scaling, and ML-specific operators
35 min
🔬
Model Deployment Lab Lab 🧪 MLOps Deployment · Intermediate
Containerize and deploy a model with FastAPI + Docker
2 hrs
📋
ML Deployment Fundamentals - Module Test Quiz
Test your knowledge of ML Deployment Fundamentals
30 min
MOD 02
CI/CD & Monitoring
Automated pipelines, drift detection, and observability
4h 30m ▾
📘
ML CI/CD Pipelines Topic
GitHub Actions, DVC, experiment tracking
32 min
📘
Model Monitoring & Drift Topic
Data drift, concept drift, performance monitoring
28 min
📘
ML Observability Stack Topic
Prometheus, Grafana, custom metrics for models
30 min
🔬
ML Pipeline Lab Lab 🧪 Monitoring & Scaling · Advanced
Build a CI/CD pipeline with automated testing and deployment
2h 30m
📋
CI/CD & Monitoring - Module Test Quiz
Test your knowledge of CI/CD & Monitoring
30 min
Live Instructor-Led Batches

Learn with a cohort — live Zoom sessions, Q&A, and lifetime access to recordings.

🐦
Early Bird — 5% off when you enroll 10+ days before your batch starts. Discount auto-applies at checkout. No code needed.
upcoming 🐦 Early Bird 5% off

MLOps & AI Infrastructure — June Cohort 2026

Instructor: AI Labs Instructor
  • 📅Jun 15, 2026 – Aug 23, 2026
  • 🕐Tuesdays & Thursdays, 7–9 PM CST
$1,999 $1,899.05 You save $100
upcoming 🐦 Early Bird 5% off

MLOps & AI Infrastructure — July Cohort 2026

Instructor: AI Labs Instructor
  • 📅Jul 15, 2026 – Sep 22, 2026
  • 🕐Tuesdays & Thursdays, 7–9 PM CST
$1,999 $1,899.05 You save $100
Ready to start?

$1,999

Online lectures + Cloud infrastructure labs + Team projects

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Frequently asked questions about this program

What level is the MLOps & AI Infrastructure program? +
Advanced. Total program length is 12 Weeks (190+ Hours of combined live instruction and lab time).
What prerequisites do I need? +
Python proficiency; Basic ML knowledge; Familiarity with Linux, Docker, and cloud platforms
Does this course include hands-on lab work? +
Yes. This program includes hands-on lab time in 5 cloud lab environments provisioned by AI Labs. Every student gets a personal cloud workspace plus on-prem workstation access at our Houston Training Center.
Is this delivered online or in person? +
Both. The default delivery is Online lectures + Cloud infrastructure labs + Team projects. In-person sessions are available at our Houston Training Center for any student who prefers on-site delivery.
What roles does this program prepare me for? +
MLOps Engineer, ML Platform Engineer, AI Infrastructure Engineer, DevOps for AI.
Do I receive a certificate at the end? +
Yes. Every program ends with a capstone project and a verifiable AI Labs completion certificate. Certificates are issued via our LMS and include the capstone work as a portfolio link.
How much does the program cost and are payment plans available? +
Program tuition is $1,999. Most students use our 2-installment plan (50% at enrollment, 50% midway through). Enterprise + nonprofit pricing is available — contact us for a quote.

Labs used in this course

Hands-on environments you'll spin up during the program.

Data Engineering Lab

Full-stack data engineering environment on GCP. Students build batch and streaming data pipelines, work with data lakes, and create feature …

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MLOps & Deployment Lab

Production ML infrastructure environment. Students build CI/CD pipelines for ML, deploy models to Kubernetes, set up monitoring, and impleme…

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LLM Inference & Serving Lab

Environment for deploying, serving, and benchmarking LLM inference. Students learn to optimize serving throughput, configure quantized model…

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GPU Training Lab

Single-GPU environment for training deep learning models, running computer vision pipelines, and experimenting with neural network architect…

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Code Server Lab

Full-featured VS Code IDE in your browser — with integrated terminal, file tree, git, extensions, and everything needed for real software en…

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