Machine Learning with Databricks Training Overview
Azure Databricks is a cloud-scale platform for data analytics and machine learning (ML). This official Microsoft course, DP-3014: Implementing a Machine Learning Solution with Azure Databricks training, teaches data scientists and ML engineers how to use Azure Databricks to implement machine learning solutions at scale.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Understand the core functionalities and applications of Azure Databricks
- Leverage Apache Spark for data processing and analysis within the Databricks environment
- Build, train, and evaluate machine learning models using various frameworks
- Implement MLflow to track experiments, manage models, and streamline deployment
- Apply advanced techniques like AutoML, hyperparameter tuning, and deep learning
- Deploy and manage machine learning models in a production setting
Prerequisites
All attendees must have experience in using Python to explore data and train machine learning models with common open-source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.
Outline
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Exploring Azure Databricks
- Introduction to Azure Databricks and its capabilities
- Key concepts and workloads
- Data governance with Unity Catalog and Microsoft Purview
- Hands-on exercise: Exploring Azure Databricks
Using Apache Spark in Azure Databricks
- Introduction to Apache Spark
- Creating and managing Spark clusters
- Working with data using Spark in notebooks
- Data visualization techniques
- Hands-on exercise: Using Spark in Azure Databricks
Training Machine Learning Models in Azure Databricks
- Machine learning principles and concepts
- Machine learning frameworks supported in Azure Databricks
- Data preparation for machine learning
- Model training and evaluation
- Hands-on exercise: Training a machine learning model
Managing the Machine Learning Lifecycle
- MLflow for experiment tracking, model registry, and deployment
- Hands-on exercise: Using MLflow
- Hyperparameter tuning with Hyperopt
- Hands-on exercise: Optimizing hyperparameters
Advanced Machine Learning Techniques
- AutoML for automated machine learning
- Hands-on exercise: Using AutoML
- Deep learning concepts and model training with PyTorch
- Distributed training with TorchDistributor
- Hands-on exercise: Training deep learning models
Productionizing Machine Learning
- Automating data transformations
- Model development and deployment strategies
- Model versioning and lifecycle management
- Hands-on exercise: Managing a machine learning model in production
Training Materials
Attendees will not need to install any software on their computers for this class. The class will be conducted in a remote environment that Accelebrate will provide; students will only need a local computer with a web browser and a stable Internet connection. Any recent version of Microsoft Edge, Mozilla Firefox, or Google Chrome will work well.
For all Microsoft Official Courses taught in their entirety that have a corresponding certification exam, an exam voucher is included for each participant.
Software Requirements
All Microsoft Azure Databricks training students receive Microsoft official courseware.
For all Microsoft Official Courses taught in their entirety that have a corresponding certification exam, an exam voucher is included for each participant.
