Implement a Machine Learning solution with Azure Databricks

Data Scientist
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.


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.

Modules in this learning path

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

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.

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.

MLflow is an open source platform for managing the machine learning lifecycle that is natively supported 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.

AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.

Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.