4.82
Course Overview
The DP-3014: Implementing a Machine Learning Solution with Azure Databricks course from Microsoft is designed to equip learners with the skills needed to effectively build and deploy machine learning models using Azure Databricks. The course covers a range of topics, starting with an introduction to Azure Databricks, its architecture, and its integration with various data sources. Participants will explore how to set up and configure a Databricks workspace, manage clusters, and work with notebooks to develop and test machine learning models.
A significant portion of the course focuses on leveraging Azure Databricks’ capabilities for data preprocessing, feature engineering, and model training. Learners will delve into machine learning libraries available within Databricks and how to use them to create predictive models. The course also covers the deployment of models into production environments, including best practices for monitoring and maintaining these models.
Throughout the course, students will gain hands-on experience with practical exercises that simulate real-world scenarios, allowing them to apply theoretical knowledge in practical contexts. By the end of the course, participants will have a comprehensive understanding of how to use Azure Databricks for end-to-end machine learning solutions, preparing them to leverage the platform’s full potential in their professional roles.
Learning Options
Schedule Dates
DP-3014: Implementing a Machine Learning solution with Azure Databricks
DP-3014: Implementing a Machine Learning solution with Azure Databricks
DP-3014: Implementing a Machine Learning solution with Azure Databricks
DP-3014: Implementing a Machine Learning solution with Azure Databricks
Course Content
- Fully-managed data lake analytics platform
- Based on open-source technologies
- Integrated with Azure for resource management and security
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
- Use MLflow to log parameters, metrics, and other details from experiment runs.
- Use MLflow to manage and deploy trained models.
- Use the Hyperopt library to optimize hyperparameters.
- Distribute hyperparameter tuning across multiple worker nodes.
- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks
- Train a deep learning model in Azure Databricks
- Distribute deep learning training by using the Horovod library
FAQs
Basic knowledge of data science and machine learning concepts is recommended. Familiarity with Azure services and Apache Spark will be beneficial but is not mandatory.
The course is divided into modules that cover:
- Introduction to Azure Databricks and its features
- Setting up and configuring Azure Databricks
- Data engineering and processing with Apache Spark
- Building and training machine learning models
- Deploying models and integrating with other Azure services
Yes, the course includes practical projects and exercises that allow you to apply the concepts learned. You will work on real-world scenarios involving data engineering, model development, and deployment using Azure Databricks.
- You can enroll in the course by visiting the CounselTrain website and following the registration process. For any further inquiries, you can contact our support team.
Azure Databricks is beneficial across various industries, including finance, healthcare, retail, and manufacturing. It helps organizations process large datasets, build predictive models, and improve decision-making through data-driven insights.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment