Design and implement data science and machine learning solutions on Azure — from workspace setup, data exploration, and model training with AutoML and notebooks to MLOps pipelines, deployment, and responsible AI — using Azure Machine Learning Studio and Python SDK v2.
From designing ML solutions and managing Azure ML workspaces to training, deploying, and operating machine learning models with full MLOps practices.
Design ML solutions, create and manage Azure ML workspaces, register datastores and data assets, provision compute clusters, explore data, train models with AutoML and the AML Designer, write training scripts with Python SDK v2, track experiments with MLflow, tune hyperparameters, build component-based pipelines, manage models, deploy to online and batch endpoints, and implement MLOps with automated retraining — the full DP-100 data science lifecycle.
Determine compute specs, describe model deployment requirements, and select the right development approach for each ML use case.
Create and manage AML workspaces, use developer tools (SDK, CLI, Studio), set up Git integration, and manage model registries.
Use AutoML for tabular, computer vision, and NLP tasks. Build no-code training pipelines with the AML Designer and custom components.
Train models using Jupyter notebooks on compute instances, track experiments with MLflow, submit training jobs using Python SDK v2, tune hyperparameters with sweep jobs, and build component-based ML pipelines.
Deploy models to online endpoints for real-time inference and batch endpoints for large-scale scoring. Test and monitor deployed services.
Trigger ML jobs from Azure DevOps and GitHub Actions, automate model retraining on data changes, and define event-based triggers.
Apply Microsoft's Responsible AI principles at every stage — evaluate models for fairness, reliability, and interpretability using AML's responsible AI dashboard, Fairlearn, and model explanations — ensuring every ML solution is ethically sound and audit-ready for enterprise deployment.
Data Science and ML are the most in-demand and best-paid skills in technology. DP-100 proves you can design and implement production ML on Azure — the platform behind millions of enterprise AI solutions.
Azure Machine Learning is used by Fortune 500 companies for mission-critical ML. DP-100 proves you can operate it at enterprise scale.
DP-100 teaches the current Azure ML Python SDK (v2) used in real production environments — not outdated tooling or just no-code GUI.
Deploying and operating models at scale with MLOps is the skill gap most enterprises struggle with. DP-100 closes that gap.
DP-100 is an Associate-level Microsoft certification — valued significantly above AI-900 and opens senior data science roles.
Azure Data Scientists consistently command salaries 30–50% above analysts and BI professionals without ML certification.
From ML solution design and workspace management to AutoML, notebook training, pipelines, model deployment, and MLOps.
Flexible pricing for video, live, and blended training modes — we reply within 24 hours.
Azure Data Scientist certification opens high-value roles in banking, healthcare, e-commerce, tech, and consulting firms globally — sectors where ML drives billions in business value.
Join data scientists and ML engineers who build, deploy, and operate production machine learning solutions on the world's leading enterprise AI platform.