Cohort 1 · completedFree
The MLOps Practitioner
Take an ML model from notebook to production: packaging and APIs, experiment tracking and CI/CD, serving at scale, drift monitoring and model optimization. Cohort 1 ran Aug–Sep 2026; four of its five lessons are recorded.
Instructor: Aya Nasser Salama
- Ran
- 16 Aug 2026 – 20 Sept 2026
- Duration
- 5 weeks
- Level
- Junior · Mid
- Language
- Arabic
- Price
- Free
Recordings
Every lesson of this cohort, free to watch.
Lesson 1
From Notebook to Production-Ready Code
3 h 10 min
Lesson 2
MLOps Core: Experiment Tracking, Versioning & Automation
2 h 55 min
Lesson 3
Inference, Serving & Release Strategies
2 h 40 min
Lesson 4
Observability & Drift Detection
3 h 15 min
- No public recording for this lesson.
Lesson 5
Model Optimization: Faster, Smaller, Cheaper
1 h 30 min
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
About this course
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.
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.
Cohort 1
Cohort 1 ran its five live lessons on YouTube Live from 16 August to 20 September 2026, with a project week and a final project. Lessons 1 to 4 are recorded; lesson 5 (Model Optimization, 20 September) has no public recording.
Course materials
- Course repository: all code, notebooks and module projects.
- Slides for every session: a Google Drive folder.
Is this course for you?
Who it's 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
Prerequisites
- 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
Syllabus
Module 1
From Notebook to Production-Ready Code
A fully containerized ML API with a test suite, structured logs, and a 3-command README.
Module 2
MLOps Core — Experiment Tracking, Versioning & Automation
A fully automated pipeline triggered by GitHub Actions — trains, evaluates against production, promotes only if metrics improve, builds a Docker image. Every run in MLflow, every dataset version in DVC.
Module 3
1st half of the project (Implementation + Revise)
No lecture. Start working on your chosen project and apply the principles from the first two lectures.
Module 4
Inference, Serving & Release Strategies
Serve the ride-duration model three ways, load test to 100 concurrent users, document the bottleneck, deploy a new version via canary rollout with automatic rollback.
Module 5
Observability & Drift Detection — Know Before Your Users Do
Module 6
Model Optimization — Faster, Smaller, Cheaper Without Losing Accuracy
Module 7
Final project
Ship the project end to end, present it to the community, and get the repo reviewed.
Instructor
Aya Nasser Salama
Founder of MLOps MENA Community and Senior MLOps Engineer
Senior MLOps & LLMOps Engineer with 6+ years in AI. At Unifonic she is the central MLOps/LLMOps support across AI teams — agentic products with LangGraph and MCP, LLM evaluation and observability, and 30B+ model serving on Kubernetes. She designed and teaches "Production ML Engineering" at ITI, and built Valeo's first production RAG system.
LinkedIn
What's included
- Live sessions on YouTube Live
- Recordings on YouTube
- Assignments
- Final project
- Certificate