Training plan
Module 1: Explore Azure Databricks
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Exercise – Explore Azure Databricks
Module 2: Use Apache Spark in Azure Databricks
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Exercise – Use Spark in Azure Databricks
Module 3: Train a machine learning model in Azure Databricks
- Understand principles of machine learning
- Machine learning in Azure Databricks
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
- Exercise – Train a machine learning model in Azure Databricks
Module 4: Use MLflow in Azure Databricks
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
- Exercise – Use MLflow in Azure Databricks
Module 5: Tune hyperparameters in Azure Databricks
- Optimize hyperparameters with Hyperopt
- Review Hyperopt trials
- Scale Hyperopt trials
- Exercise – Optimize hyperparameters for machine learning in Azure Databricks
Module 6: Use AutoML in Azure Databricks
- What is AutoML?
- Use AutoML in the Azure Databricks user interface
- Use code to run an AutoML experiment
- Exercise – Use AutoML in Azure Databricks
Module 7: Train deep learning models in Azure Databricks
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with TorchDistributor
- Exercise – Train deep learning models on Azure Databricks
Module 8: Manage machine learning in production with Azure Databricks
- Automate your data transformations
- Explore model development
- Explore model deployment strategies
- Explore model versioning and lifecycle management
- Exercise – Manage a machine learning model
Recommended Prerequisite Knowledge
- Experience in Python programming for Machine Learning
- Knowledge of fundamental ML and Data Science concepts
- Understanding of ML algorithms and optimization techniques
- Familiarity with Azure Databricks and its environment
- Experience with ML libraries (scikit-learn, TensorFlow, PyTorch)
- Knowledge of Big Data principles and distributed processing
- Understanding of ML pipelines and MLOps
- Experience with versioning and experiment tracking
- Familiarity with cloud computing concepts
- Basic knowledge of SQL and data manipulation
Eccentrix Corner article/publication
Implementing a Machine Learning Solution with Azure Databricks (DP-3014)
The Implementing a Machine Learning Solution with Azure Databricks (DP-3014) training is designed for data engineers, data scientists, and IT professionals looking to design and deploy advanced Machine Learning solutions using Azure Databricks. This course focuses on data preparation, modeling, and deploying models while leveraging Azure Databricks’ advanced analytics and integration pipelines.
Participants will gain essential skills to develop and integrate efficient Machine Learning solutions into complex cloud environments.
Why Take This Training?
Azure Databricks is a powerful platform for data engineering and Machine Learning. By taking this course, you will learn to use Databricks’ advanced features to develop accurate predictive models and automated workflows while seamlessly integrating your solutions into cloud environments.
This course prepares you to tackle modern challenges in data management and analytics, transforming your Machine Learning capabilities.
Skills Developed During the Training
Data Preparation and Management
Learn to clean, transform, and organize large datasets for Machine Learning models.Model Design and Deployment
Master techniques for designing predictive models and deploying them efficiently in cloud environments.Optimizing Machine Learning Workflows
Automate Machine Learning pipelines to enhance performance and productivity.Utilizing Azure Databricks Features
Leverage Azure Databricks’ advanced tools to manage complex Machine Learning projects.Collaboration and Integration
Integrate your solutions with other Azure services and collaborate effectively with multidisciplinary teams.Advanced Analytics and Reporting
Develop interactive visualizations and reports based on Machine Learning models.
Practical, Expert-Led Training
This training is delivered by certified Azure instructors who combine theoretical knowledge with practical exercises. Participants will work on real-world scenarios to acquire skills that can be immediately applied in professional environments.
Who Should Attend?
- Data engineers responsible for Machine Learning projects
- Data scientists seeking to optimize their workflows
- Cloud architects aiming to integrate Machine Learning models into existing solutions
- IT professionals looking to specialize in advanced analytics and Machine Learning
Master Machine Learning with Azure Databricks
The Implementing a Machine Learning Solution with Azure Databricks (DP-3014) training equips you to fully leverage Azure’s capabilities for designing and deploying advanced solutions. Enroll today to transform your Machine Learning skills and deliver concrete results for your organization.
Frequently asked questions - Azure ML Databricks training (FAQ)
What topics are covered in this training?
Data preparation, modeling, optimizing Machine Learning workflows, and Azure integration.
What tools are used in this course?
Participants will work with Azure Databricks, AutoML, and other Azure tools.
Is prior experience in Machine Learning required?
A basic understanding of Machine Learning concepts and data engineering is recommended.
Does the training include hands-on exercises?
Yes, interactive exercises and real-world scenarios are included to reinforce learning.
How can this training benefit my organization?
It enables the development of robust, integrated Machine Learning solutions tailored to your business needs.
Are the concepts taught applicable to other cloud platforms?
While the training focuses on Azure, many principles can be adapted to other cloud environments.
