Name: The MLOps Practitioner
Availability: InStock
Rating: 5 (3 reviews)

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 retraining. They will serve predictions at scale using FastAPI, BentoML, Triton, and vLLM, monitor for drift before users notice, and optimize models for GPU, CPU, and edge devices. They will think and work like production ML engineers.

5 live lessons7 WeeksCohort 1

5.0 (3)

Aya Nasser Salama

Senior MLOps Engineer

What you'll 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

Course 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

Description

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.

Most ML engineers know how to train a model. Very few know how to package it, test it, and expose it as a reliable API that other systems can depend on. This module closes that gap — transforming messy notebook code into a professional, containerized, testable ML service.

Module Content (lessons):

Lesson 1 — The MLOps Maturity Model
Where most teams are vs where they should be. The five levels from "notebook only" to "fully automated ML."

Lesson 2 — Python Packaging & Project Structure
pyproject.toml, OOP for ML, type hints, decorators, and why project structure matters in production.

Lesson 3 — Building ML APIs
FastAPI vs Litestar — when to use each, building a /predict endpoint, request/response schemas with Pydantic.

Lesson 4 — Serialization Formats
JSON, Protobuf, Pickle, ONNX — what each is, when to use which, and what format your model should be served in.

Lesson 5 — Docker & Containerization
Multi-stage builds, docker-compose, why containers are the unit of deployment in MLOps.

Lesson 6 — Structured Logging
JSON logs, correlation IDs, log levels — why print() statements are not enough in production.

Lesson 7 — Testing ML Code with pytest
Fixtures, mocks, coverage gates — how to write tests for an ML API that actually catch bugs.

Module Project:
A fully containerized ML API with a test suite, structured logs, and a 3-command README that any team member can run from scratch.

Instructors

Aya Nasser Salama

Senior MLOps Engineer

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.