DP-3014 | Implementing a Machine Learning solution with Azure Databricks
 
Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
 
Prerequisites
This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.
 
Course Outline
Module 1: Explore Azure Databricks
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Introduction
Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Exercise - Explore Azure Databricks
Knowledge check
Summary
Module 2: Use Apache Spark in Azure Databricks
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
Introduction
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
Knowledge check
Summary
Module 3: Train a machine learning model in Azure Databricks
Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.
Introduction
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
Knowledge check
Summary
Module 4: Use MLflow in Azure Databricks
MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.
Introduction
Capabilities of MLflow
Run experiments with MLflow
Register and serve models with MLflow
Exercise - Use MLflow in Azure Databricks
Knowledge check
Summary
Module 5: Tune hyperparameters in Azure Databricks
Tuning hyperparameters is an essential part of machine learning. In Azure Databricks, you can use the Hyperopt library to optimize hyperparameters automatically.
Introduction
Optimize hyperparameters with Hyperopt
Review Hyperopt trials
Scale Hyperopt trials
Exercise - Optimize hyperparameters for machine learning in Azure Databricks
Knowledge check
Summary
Module 6: Use AutoML in Azure Databricks
AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
Introduction
What is AutoML?
Use AutoML in the Azure Databricks user interface
Use code to run an AutoML experiment
Exercise - Use AutoML in Azure Databricks
Knowledge check
Summary
Module 7: Train deep learning models in Azure Databricks
Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.
Introduction
Understand deep learning concepts
Train models with PyTorch
Distribute PyTorch training with Horovod
Exercise - Train deep learning models on Azure Databricks
Knowledge check
Summary
 
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