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.
After completing this course, students will be able to:
Provision an Azure Databricks workspace
Identify core workloads and personas for Azure Databricks
Describe key concepts of an Azure Databricks solution
Describe key elements of the Apache Spark architecture
Create and configure a Spark cluster
Describe use cases for Spark
Use Spark to process and analyse data stored in files
Use Spark to visualise data
Prepare data for machine learning
Train a machine learning model
Evaluate a machine learning model
Use MLflow to log parameters, metrics, and other details from experiment runs
Use MLflow to manage and deploy trained models
Use the Hyperopt library to optimise hyperparameters
Distribute hyperparameter tuning across multiple worker nodes
Use the AutoML user interface in Azure Databricks
Use the AutoML API in Azure Databricks
Train a deep learning model in Azure Databricks
Distribute deep learning training by using the Horovod library
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Data Scientists
Machine Learning Engineers
Experience using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
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