# The MLOps Practitioner

> By the end of this course, students will take a machine learning model from notebook to production — building automated CI/CD pipelines, experiment tracking, and scheduled…

- **URL:** https://zomra.io/courses/the-mlops-practitioner
- **Type:** Course
- **Published:** July 22, 2026
- **Last updated:** July 27, 2026
- **Category:** AI
- **Price:** Free
- **Taught in:** English
- **Students enrolled:** 504
- **Rating:** 4.8 out of 5 from 4 reviews

## About this course

Most ML engineers know how to train a model. Almost none know how to ship it.

This course bridges the gap between data science and production engineering. You will take a machine learning model from a research notebook all the way to a live, monitored, auto-retrained production system — step by step, with real code and real tools used by companies like Uber, Spotify, Meta, and Netflix.

Across 5 sessions you will learn how to:

- Package ML code professionally and build REST APIs with FastAPI and Litestar — fully containerized with Docker and tested with pytest

- Track experiments with MLflow, version data with DVC, and automate your entire pipeline with GitHub Actions and Terraform

- Orchestrate retraining with Apache Airflow, serve models at scale using BentoML, Triton, and vLLM, and release safely with canary and shadow deployments

- Monitor production models for data drift, concept drift, and embedding drift using Evidently AI, Prometheus, Grafana, and Langfuse

- Optimize models for speed and size using pruning, quantization, knowledge distillation, TensorRT, and OpenVINO — and measure every tradeoff

Every session ends with a deployable project that builds on the previous one. By the end you will have a full MLOps portfolio that demonstrates real production engineering skills.

## What you will learn

- Structure ML projects professionally using Python packaging, OOP, type hints, and build production-grade REST APIs with FastAPI or Litestar — containerized with Docker and tested with pytest
- Track experiments, version data, and manage model lifecycle using MLflow and DVC — and automate the full train → test → build → push pipeline with GitHub Actions and Terraform
- Implement Continuous Training pipelines that automatically retrain, evaluate, and promote models to production when data drifts or performance degrades — without any human intervention
- Choose the right inference pattern and serve models in production using the full serving stack: FastAPI → BentoML → TensorRT/Triton for GPU → ONNX Runtime/OpenVINO for CPU → vLLM for LLMs
- Release models safely using canary rollouts, A/B testing, blue/green deployments, and shadow mode — with automatic rollback when metrics degrade
- Detect data drift, concept drift, label drift, and embedding drift using PSI, KS test, Page-Hinkley, and MMD — and monitor production systems with Prometheus, Grafana, Langfuse, and RAGAS
- Optimize trained models using pruning, quantization (PTQ and QAT), knowledge distillation, TensorRT, OpenVINO, and TFLite — measuring the accuracy vs latency vs size tradeoff at every step

## Who this course is for

- ML engineers and data scientists who can train models but struggle to deploy and maintain them in production
- Software engineers, DevOps Engineers, transitioning into MLOps or AI infrastructure roles who want a structured, hands-on path
- Technical leads and architects who need to understand the full ML production stack to make better tooling and infrastructure decisions

## Requirements

- Basic Python programming knowledge — you should be comfortable writing functions, classes, and working with libraries like pandas and scikit-learn
- Familiarity with machine learning concepts — you should have trained at least one model before (linear regression, classification, etc.)
- A laptop with Docker installed and at least 8GB RAM — all tools used are free and open-source

## What's included

- Interactive live lessons
- Projects to apply learnings
- Community of peers
- Certificate of completion
- Lifetime access to all course materials

## Cohorts

- **Cohort 1** — August 15, 2026 → September 26, 2026 · 7 weeks · 5 live lessons

## Instructor

### Aya Nasser Salama

Founder of MLOps MENA Community and Senior MLOps Engineer @ Unifonic

I'm a Senior MLOps and LLMOps Engineer with 6+ years in AI, holding a master's in Informatics from Nile University. I've worked at Unifonic, Valeo, Aiactive Technologies, and Advanced Programs Co. I'm an MLOps instructor at ITI Cairo and founder of the MLOps MENA Community.

- https://www.linkedin.com/in/ayanasser/
- https://www.linkedin.com/company/mlops-mena/

## Frequently asked questions

### What happens if I can't make a live session?

All live sessions are recorded and available for replay within 24 hours. You can watch them at your convenience.

### Will I receive a certificate after completing the course?

Yes, you will receive a certificate of completion after finishing all required modules and assignments.

### What is your refund policy?

We offer a 14-day money-back guarantee. If you're not satisfied with the course, you can request a full refund within 14 days of enrollment.

## Enrolment

Enrol at https://zomra.io/courses/the-mlops-practitioner