Dp-3014: Implementing a Machine Learning Solution With Azure Databricks

The DP-3014: Implementing a Machine Learning Solution with Azure Databricks course teaches professionals how to build, train, and deploy machine learning models using Azure Databricks and Apache Spark MLlib. This 8-hour hands-on course covers data preprocessing, model training and evaluation, MLflow lifecycle management, hyperparameter tuning with Hyperopt, AutoML, and production deployment strategies. Esamatic srl, a Microsoft Learning Partner in Milan, delivers this course with Microsoft Certified Trainers.

• Applied Skills Credential: validates competency in implementing ML solutions with Azure Databricks
• Spark MLlib: distributed machine learning algorithms for classification, regression, and clustering
• MLflow Lifecycle Management: experiment tracking, model versioning, and artifact management
• Hyperparameter Tuning: Hyperopt integration for automated hyperparameter optimization
• AutoML and Deployment: automated model selection and production deployment strategies

Course Overview: DP-3014 Machine Learning with Azure Databricks

Azure Databricks provides a collaborative environment for machine learning at scale, combining Apache Spark’s distributed computing with enterprise MLOps capabilities. The DP-3014 course provides end-to-end experience building ML solutions — from data preparation and feature engineering through model training, hyperparameter optimization, and deployment using MLflow and Databricks’ managed ML infrastructure.

Learning Objectives

1. Prepare data for machine learning — perform data cleaning, feature engineering, and train-test splitting using Spark DataFrames
2. Train and evaluate models with MLlib — build classification, regression, and clustering models using Spark MLlib pipelines
3. Optimize models with Hyperopt — automate hyperparameter tuning using distributed search strategies
4. Manage and deploy models with MLflow — track experiments, register models, and deploy to production endpoints

Who Should Attend

This course is designed for data scientists, ML engineers, and data engineers working with large datasets who want to implement scalable machine learning solutions using Azure Databricks.

Career Benefits

Scalable ML on distributed platforms is essential for enterprise AI. The DP-3014 Applied Skills credential validates practical ability to implement ML solutions with Databricks — a competency valued for machine learning engineers, data scientists, and AI platform specialists working at enterprise scale.

Prerequisites

• Intermediate Python programming experience
• Understanding of machine learning concepts and algorithms
• Familiarity with Azure Databricks or Apache Spark basics
• Experience with data manipulation and analysis

Frequently Asked Questions

What is the DP-3014 Applied Skills credential?

The DP-3014 is a Microsoft Applied Skills credential that validates hands-on ability to implement machine learning solutions using Azure Databricks. It is earned through a performance-based lab assessment.

How does DP-3014 differ from DP-3007?

DP-3007 focuses on Azure Machine Learning’s managed platform. DP-3014 focuses on Azure Databricks with Spark MLlib. Both cover ML model training and deployment but on different platforms.

Do I need Spark experience for DP-3014?

Basic Spark or Databricks familiarity is helpful. The course covers Spark MLlib from a practical perspective, but understanding of Python and ML fundamentals is essential.

Does the DP-3014 credential expire?

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