Dp-3007: Train and Deploy a Machine Learning Model With Azure Machine Learning

The DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning course teaches professionals how to build, train, and deploy machine learning models using Azure Machine Learning. This 8-hour hands-on course covers workspace setup, compute configuration, training script development, experiment tracking with MLflow, and model deployment to real-time endpoints using the Python SDK. Esamatic srl, a Microsoft Learning Partner in Milan, delivers this course with Microsoft Certified Trainers.

• Applied Skills Credential: validates competency in training and deploying ML models with Azure Machine Learning
• Azure ML Workspace: environment setup, data assets, and compute target configuration
• Model Training: training scripts, experiment jobs, and hyperparameter tuning
• MLflow Integration: experiment tracking, metric logging, and model registry management
• Model Deployment: real-time endpoints, managed online deployments, and inference testing

Course Overview: DP-3007 Train and Deploy ML Models with Azure ML

Azure Machine Learning is Microsoft’s enterprise-grade platform for building, training, and deploying machine learning models at scale. The DP-3007 course provides end-to-end experience with the ML lifecycle — from workspace configuration and data preparation through model training, evaluation, and production deployment using managed endpoints and the Azure ML Python SDK.

Learning Objectives

1. Set up Azure ML workspace and compute — configure workspaces, create compute instances and clusters, and manage data connections
2. Train machine learning models — write training scripts, submit experiment jobs, and perform hyperparameter tuning
3. Track experiments with MLflow — log metrics, parameters, and artifacts using MLflow integration for reproducible ML workflows
4. Deploy models to endpoints — register models, create managed online endpoints, and test real-time inference

Who Should Attend

This course is designed for data scientists, ML engineers, and developers seeking to operationalize machine learning models using Azure Machine Learning’s managed platform.

Career Benefits

MLOps and model deployment skills bridge the gap between experimentation and production AI. The DP-3007 Applied Skills credential validates practical ability to train and deploy ML models on Azure — a competency essential for machine learning engineers, data scientists, and AI platform specialists.

Prerequisites

• Intermediate Python programming experience
• Basic understanding of machine learning concepts and algorithms
• Familiarity with Azure portal and resource management
• Experience with data manipulation using pandas or similar libraries

Frequently Asked Questions

What is the DP-3007 Applied Skills credential?

The DP-3007 is a Microsoft Applied Skills credential that validates hands-on ability to train and deploy machine learning models using Azure Machine Learning. It is earned through a performance-based lab assessment.

What is MLflow?

MLflow is an open-source platform for managing the ML lifecycle, including experiment tracking, model packaging, and deployment. Azure Machine Learning provides native MLflow integration for streamlined workflows.

Do I need data science experience for DP-3007?

Basic ML concepts and Python skills are required. You should understand supervised learning, model evaluation, and Python data libraries. The course focuses on Azure ML platform skills rather than ML theory.

Does the DP-3007 credential expire?

Microsoft Applied Skills credentials are valid for one year from the date earned and can be renewed through reassessment.