DP-3014: Implementing a Machine Learning solution with Azure Databricks
Develop machine learning solutions with Azure Databricks. DP-3014 equips you with the skills to build, train, and deploy machine learning models using Apache Spark MLlib on Databricks. Learn about machine learning concepts, model development techniques, and model deployment for real-world applications.
Instructor
DP-3014: Implementing a Machine Learning solution with Azure Databricks Training
DP-3014: Implementing a Machine Learning solution with Azure Databricks
The DP-3014: Implementing a Machine Learning Solution with Azure Databricks Course provides hands-on training in using Azure Databricks and Apache Spark for machine learning. Participants will learn to prepare data, train models, and tune hyperparameters using tools like MLflow and AutoML. This Azure Databricks for Machine Learning training focuses on building scalable machine learning solutions. Enroll in the DP-3014 course to enhance your machine learning expertise with Azure Databricks.
Target Audience
Data Engineer
Data Scientist
Prerequisites Required:
Have experience using Python to explore data and train machine learning models with common open-source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.
Course Objectives
Understanding Azure Databricks for Machine Learning
Setting Up Azure Databricks
Data Processing and Exploration
Machine Learning with Databricks
Building and Tuning ML Models
Deploying ML Models
Collaborative ML Development
Best Practices and Performance Optimization
Course Outline
1) Explore Azure Databricks
Provision an Azure Databricks workspace
Identify core workloads and personas for Azure Databricks
Describe key concepts of an Azure Databricks solution
Lab: Explore Azure Databricks
2) Use Apache Spark in Azure Databricks
Describe key elements of the Apache Spark architecture
Create and configure a Spark cluster
Describe use cases for Spark
Use Spark to process and analyze data stored in files
Use Spark to visualize data
Lab: Use Spark in Azure Databricks
3) Train a machine learning model in Azure Databricks
Prepare data for machine learning
Train a machine learning model
Evaluate a machine learning model
Lab: Train a machine learning model in Azure Databricks
4) Use MLflow in Azure Databricks
Use MLflow to log parameters, metrics, and other details from experiment runs
Use MLflow to manage and deploy trained models
Lab: Use MLflow in Azure Databricks
5) Tune hyperparameters in Azure Databricks
Use the Hyperopt library to optimize hyperparameters
Distribute hyperparameter tuning across multiple worker nodes
Lab: Optimize hyperparameters for machine learning in Azure Databricks
6) Use AutoML in Azure Databricks
Use the AutoML user interface in Azure Databricks
Use the AutoML API in Azure Databricks
Lab: Use AutoML in Azure Databricks
7) Train deep learning models in Azure Databricks
Train a deep learning model in Azure Databricks
Distribute deep learning training by using the Horovod library
Lab: Train deep learning models on Azure Databricks
The DP-3014: Implementing a Machine Learning Solution with Azure Databricks Course provides hands-on training in using Azure Databricks and Apache Spark for machine learning. Participants will learn to prepare data, train models, and tune hyperparameters using tools like MLflow and AutoML. This Azure Databricks for Machine Learning training focuses on building scalable machine learning solutions. Enroll in the DP-3014 course to enhance your machine learning expertise with Azure Databricks.
Target Audience
Data Engineer
Data Scientist
Prerequisites Required:
Have experience using Python to explore data and train machine learning models with common open-source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.
Course Objectives
Understanding Azure Databricks for Machine Learning
Setting Up Azure Databricks
Data Processing and Exploration
Machine Learning with Databricks
Building and Tuning ML Models
Deploying ML Models
Collaborative ML Development
Best Practices and Performance Optimization
Course Outline
1) Explore Azure Databricks
Provision an Azure Databricks workspace
Identify core workloads and personas for Azure Databricks
Describe key concepts of an Azure Databricks solution
Lab: Explore Azure Databricks
2) Use Apache Spark in Azure Databricks
Describe key elements of the Apache Spark architecture
Create and configure a Spark cluster
Describe use cases for Spark
Use Spark to process and analyze data stored in files
Use Spark to visualize data
Lab: Use Spark in Azure Databricks
3) Train a machine learning model in Azure Databricks
Prepare data for machine learning
Train a machine learning model
Evaluate a machine learning model
Lab: Train a machine learning model in Azure Databricks
4) Use MLflow in Azure Databricks
Use MLflow to log parameters, metrics, and other details from experiment runs
Use MLflow to manage and deploy trained models
Lab: Use MLflow in Azure Databricks
5) Tune hyperparameters in Azure Databricks
Use the Hyperopt library to optimize hyperparameters
Distribute hyperparameter tuning across multiple worker nodes
Lab: Optimize hyperparameters for machine learning in Azure Databricks
6) Use AutoML in Azure Databricks
Use the AutoML user interface in Azure Databricks
Use the AutoML API in Azure Databricks
Lab: Use AutoML in Azure Databricks
7) Train deep learning models in Azure Databricks
Train a deep learning model in Azure Databricks
Distribute deep learning training by using the Horovod library
Lab: Train deep learning models on Azure Databricks
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