MLOps | Machine Learning Operations (Duke University Coursera Specialization)

2026-04-17 · 4min · Pragmatic AI Labs

Table of Contents

Four courses covering the MLOps lifecycle from Python foundations through DevOps and DataOps practices to SageMaker, Azure ML, MLflow, and the Hugging Face toolchain. The Duke University curriculum for engineers operationalizing machine learning.

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What You Will Build

Python-native ML pipelines, DevOps-hardened training and deployment workflows, SageMaker and Azure ML model deployments, and MLflow-tracked experiments with Hugging Face integration.

Courses in This Specialization

Python Essentials for MLOps — Python packaging, testing, and the tooling an MLOps engineer uses daily.
DevOps, DataOps, MLOps — CI/CD for data and ML systems; reproducibility; observability.
MLOps Tools: MLflow and Hugging Face — Experiment tracking, model registry, and integration with the Hugging Face Hub.
MLOps Platforms: Amazon SageMaker and Azure ML — Managed MLOps platforms compared; when to use which.

Who This Is For

Data scientists moving models into production
DevOps engineers adding ML workloads to their charter
Platform engineers building internal MLOps platforms

Enterprise AI and Data Engineering with Databricks — lakehouse-native MLOps
Building Cloud Computing Solutions at Scale — cloud foundations prerequisite
Large Language Model Operations (LLMOps) — MLOps extended to LLMs

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