Machine learning with Azure Databricks (DP-3014)
Course Outline
Embark on an enriching journey with this hands-on instructor-led Microsoft course, 'Machine Learning with Azure Databricks (DP-3014),' designed to empower you with cloud-scale capabilities for data analytics and machine learning. Within this immersive one-day experience, you'll delve into Azure Databricks, a versatile platform enabling data scientists and machine learning engineers to implement robust solutions at scale, revolutionizing the way data insights are extracted and utilized.
Machine learning with Azure Databricks (DP-3014) Benefits
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In this course, you will learn how to:
- Gain proficiency in utilizing Azure Databricks, a cloud service offering a scalable platform for data analytics using Apache Spark.
- Acquire practical knowledge and hands-on experience in employing Spark to transform, analyze, and visualize data at scale.
- Develop skills in training machine learning models and evaluating their performance within the Azure Databricks environment.
- Learn to leverage MLflow, an open-source platform for managing the machine learning lifecycle, seamlessly integrated with Azure Databricks.
- Master the art of hyperparameter tuning and optimization using Hyperopt library, enhancing the efficiency of machine learning workflows.
- Explore the simplicity and effectiveness of AutoML in Azure Databricks for automating the model building process.
- Dive into the realm of deep learning, understanding concepts and training models for complex AI workloads like forecasting, computer vision, and natural language processing.
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Training Prerequisites
To fully benefit from this course, please ensure you possess proficiency in Python for data exploration and machine learning model training using popular open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
Machine learning with Azure Databricks Training Outline
Learning Objectives
- Explore Azure Databricks
- Introduction to Azure Databricks as a cloud service providing a scalable platform for data analytics.
- Use of Apache Spark in Azure Databricks for performing data transformations, analysis, and visualizations at scale.
- Train a Machine Learning Model in Azure Databricks
- Understanding how data is used for training predictive models in Azure Databricks.
- Overview of the commonly used machine learning frameworks supported by Azure Databricks.
- Use MLflow in Azure Databricks
- Introduction to MLflow as an open-source platform managing the machine learning lifecycle.
- Insight into how MLflow is natively supported in Azure Databricks.
- Tune Hyperparameters in Azure Databricks
- The important role of tuning hyperparameters in machine learning.
- Using the Hyperopt library in Azure Databricks for automated hyperparameters optimization.
- Use AutoML in Azure Databricks
- An overview of AutoML’s role in simplifying the process of building effective machine learning models.
- Insight into how AutoML fits into the Azure Databricks ecosystem.
- Train Deep Learning Models in Azure Databricks
- Understanding deep learning and its use of neural networks for training machine learning models.
- Looking at the complex forecasting, computer vision, natural language processing, and other AI workloads handled by deep learning in Azure Databricks.
- choosing a selection results in a full page refresh