Machine learning with Azure Databricks (DP-3014)

Course 8686

Duration: 1 day
Language: English
Level: Intermediate

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 Delivery Methods

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Machine learning with Azure Databricks Training Information

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.

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.

Get This Course £835

1-Day Instructor-Led Training
Hands-On Labs
Microsoft Official Content
After-Course Instructor Coaching Included
Available for Private Team Training

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Machine learning with Azure Databricks FAQs

Azure Databricks provides a seamless integration of Apache Spark with cloud services, offering a scalable platform tailored for data scientists and machine learning engineers. Its collaborative workspace, optimized Spark performance, and built-in support for machine learning frameworks streamline the process of implementing solutions at scale.

MLflow simplifies the management of the machine learning lifecycle by providing capabilities for tracking experiments, packaging code, and sharing models. Integrated natively with Azure Databricks, MLflow enables users to efficiently run experiments, register models, and deploy them seamlessly into production environments, facilitating collaboration and reproducibility.

AutoML in Azure Databricks automates the process of building machine learning models, enabling users to quickly experiment with various algorithms and hyperparameters without extensive manual intervention. By leveraging AutoML, users can accelerate the model development process, optimize model performance, and focus on interpreting and utilizing the generated insights.

Azure Databricks provides a comprehensive environment for training deep learning models, including support for popular frameworks like PyTorch. With capabilities for distributed training using tools like Horovod, users can efficiently scale their deep learning workflows to handle large datasets and complex AI workloads, empowering them to unlock new possibilities in areas such as computer vision and natural language processing.

Machine learning with Azure Databricks (DP-3014)

Machine learning with Azure Databricks Delivery Methods

Machine learning with Azure Databricks Training Information

Machine learning with Azure Databricks Training Outline

Learning Objectives

Machine learning with Azure Databricks FAQs

What distinguishes Azure Databricks from other cloud-scale platforms for data analytics and machine learning?

How does MLflow enhance the machine learning lifecycle within Azure Databricks?

What are the key advantages of using AutoML in Azure Databricks for model development?

How does Azure Databricks support the training of deep learning models?

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