TrainingsAI in Production Bootcamp: MLOps & LLMOps from Zero to Scale

AI in Production Bootcamp: MLOps & LLMOps from Zero to Scale

Master end-to-end MLOps by building, deploying, automating, monitoring, and maintaining production-ready ML systems using Git, CI/CD, Docker, Kubernetes, Azure, MLflow, DVC, Airflow, and advanced drift detection practices.

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2 students enrolled
16 hours
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Quick Facts

Students Enrolled2
Available Seats38
Course Duration16h
FormatOFFLINE
AI in Production Bootcamp: MLOps & LLMOps from Zero to Scale
25,000 PKR80% OFF
5,000PKR

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This course includes:

16 hours on-demand content
8 lessons
Certificate of completion
Access on mobile and desktop
Community support
Enrollment2/40

About this course

Machine learning operations, MLOps, are best practices for businesses to run AI successfully with help from an expanding smorgasbord of software products and cloud services.

What you'll learn

Understand and apply core Git workflows for collaborative ML projects

Build and test Python applications using virtual environments, Makefiles, linting, and unit testing

Develop and deploy Flask-based ML services

Automate CI/CD pipelines using GitHub Actions and Jenkins

Containerize applications using Docker and manage multi-service systems with Docker Compose

Deploy ML systems on Azure using automated pipelines

Understand and operate Kubernetes using kubectl and Minikube

Apply MLOps maturity levels to real-world ML systems

Track experiments and models using MLflow

Version datasets and pipelines using DVC

Orchestrate ML workflows using Apache Airflow

Monitor systems using Prometheus, Grafana, and Alertmanager

Detect and respond to ML Drift (data, concept, label, feature)

Implement production-grade monitoring and alerting for ML systems

Course Curriculum

Expand each module to see what you'll learn

8

Modules

16h 0m

Total Duration

1

Introduction to MLOps & ML Lifecycle

MODULE
8 learning points
120 min
2

Git & GitHub for Machine Learning

MODULE
8 learning points
120 min
3

CI/CD for ML using GitHub Actions

MODULE
8 learning points
120 min
4

Environment Management & Code Quality

MODULE
8 learning points
120 min
5

Testing in Machine Learning

MODULE
8 learning points
120 min
6

Experiment Tracking & Data Versioning

MODULE
8 learning points
120 min
7

Model Deployment & Containerization

MODULE
8 learning points
120 min
8

Monitoring, Drift & Final Project Presentation

MODULE
10 learning points
120 min

Requirements

  • Basic Python programming knowledge

  • Understanding of machine learning fundamentals

  • Familiarity with command-line tools

  • Basic knowledge of Linux environment

  • Introductory understanding of software development concepts

  • Basic understanding of cloud concepts (helpful but not mandatory)