Course Description: 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 deploy machine learning solutions at scale.

• Level: Intermediate
• Product: Azure Databricks
• Role: Data Scientist
  

Training Route

• Explore Azure Databricks - Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
• Using Apache Spark on 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.
• Training a Machine Learning Model in Azure Databricks: Machine learning involves using data to train a predictive model. Azure Databricks supports several commonly used machine learning frameworks that you can use to train models.
• Using MLflow in Azure Databricks: MLflow is an open-source platform for managing the machine learning lifecycle that Azure Databricks natively supports.
• Hyperparameter tuning in Azure Databricks: Hyperparameter tuning is an essential part of machine learning. In Azure Databricks, you can use the Hyperopt library to automatically optimize hyperparameters.
• Using AutoML in Azure Databricks: AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
• Training 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.

Prerequisites

Experience using Python to explore data and train machine learning models with common open source frameworks such as S cikit-Learn, PyTorch, and TensorFlow is recommended.

Language

• Course: English / Spanish
• Labs: English / Spanish