Training: Implementing a Machine Learning Solution with Azure Databricks (DP-3014)

Ref. DP-3014

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Duration:

1

day

Exam:

Not certifying

Level:

Intermediate

Funding:

Eligible

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Implementing a Machine Learning Solution with Azure Databricks (DP-3014)

Our training course ‘Implementing a Machine Learning Solution with Azure Databricks (DP-3014)’ will unveil all the secrets of Machine Learning with Azure Databricks (DP-3014), specifically designed to equip you with advanced data analysis and machine learning skills at the cloud scale. This one-day course, led by experts, offers a full immersion into Azure Databricks, a versatile platform that revolutionizes the implementation of robust solutions for data scientists and machine learning engineers.

By covering modules from exploring Azure Databricks and using Apache Spark, to training deep learning models with PyTorch, this course: Implementing a Machine Learning Solution with Azure Databricks (DP-3014), prepares you to fully leverage the potential of your data and innovate in your professional projects.

Participant Profiles

Data Scientists
Data Analysts
Machine Learning Engineers
Software Developers
Computer Scientists

Objectives

Master Azure Databricks for data analysis with Apache Spark.
Gain hands-on experience in transforming, analyzing, and visualizing large-scale data.
Develop skills in training machine learning models and evaluating their performance.
Use MLflow effectively for managing the machine learning lifecycle.
Optimize machine learning workflows with hyperparameter tuning via Hyperopt.
Explore the automation of model building with AutoML in Azure Databricks.

Prerequisites

Proficiency in Python for data exploration and training machine learning models.
Knowledge of popular open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.

Course Content

Module 1: Explore Azure Databricks

Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Data governance using Unity Catalog and Microsoft Purview

Module 2: Use Apache Spark in Azure Databricks

Discover Spark
Create a Spark cluster
Use Spark in notebooks
Use Spark to work with data files
Visualize data

Module 3: Train a Machine Learning model with Azure Databricks

Understand the principles of Machine Learning
Machine Learning in Azure Databricks
Prepare data for Azure Machine Learning
Train a Machine Learning model
Evaluate a Machine Learning model

Module 4: Use MLflow in Azure Databricks

MLflow features
Run experiments with MLflow
Register and serve models with MLflow

Module 5: Tune hyperparameters in Azure Databricks

Optimize hyperparameters with Hyperopt
Review Hyperopt trials
Scale Hyperopt trials

Module 6: Use AutoML in Azure Databricks

What is AutoML?
Use AutoML in the Azure Databricks UI
Use code to run an AutoML experiment

Module 7: Train Deep Learning models in Azure Databricks

Understand Deep Learning concepts
Train models with PyTorch
Distribute PyTorch training with TorchDistributor

Module 8: Manage Machine Learning in production with Azure Databricks

Automate your data transformations
Explore model development
Explore model deployment strategies
Manage model versions and lifecycle

Documentation

Access to Microsoft Learn, Microsoft’s online learning platform, offering interactive resources and educational content to deepen your knowledge and develop your technical skills.

Lab / Exercises

This course provides you with exclusive access to the official Microsoft lab, enabling you to practice your skills in a professional environment.

Complementary Courses

Data Engineering on Microsoft Azure (DP-203) | DP-203T00

Eligible Funding

ITTA is a partner of a continuing education fund dedicated to temporary workers. This fund can subsidize your training, provided that you are subject to the “Service Provision” collective labor agreement (CCT) and meet certain conditions, including having worked at least 88 hours in the past 12 months.

Additional Information

Introduction to Azure Databricks: Implementing a Machine Learning Solution with Azure Databricks (DP-3014)

Azure Databricks distinguishes itself as a platform of choice for data science and machine learning professionals, thanks to its unique integration of Apache Spark with cloud services. This synergy creates an optimal collaborative environment, enhances Spark’s performance, and facilitates the implementation of complex machine learning solutions on a large scale. This distinction of Azure Databricks lies in its ability to offer a collaborative and efficient platform, specifically designed for data analysis and machine learning.

Exploit the Power of Apache Spark

With Apache Spark, Azure Databricks revolutionizes large-scale data processing, allowing users to easily manage clusters, run interactive notebooks, and handle massive data sets. Data visualization becomes effortless, providing valuable insights for informed decision-making.

Simplified Machine Learning

Azure Databricks makes machine learning more accessible by providing an intuitive framework for data preparation, model training, and evaluation. With MLflow, simplified management of the machine learning lifecycle is at your fingertips, enabling experiment tracking, code management, and model sharing, which enhances collaboration and ensures the reproducibility of projects.

Optimization and Automation with Hyperparameters and AutoML

Optimizing hyperparameters with Hyperopt and using AutoML to automate the creation of machine learning models demonstrate Azure Databricks’ ability to simplify and accelerate model development. AutoML, in particular, changes the game by allowing rapid experimentation with different algorithms and hyperparameters, thus minimizing the need for manual interventions and focusing on optimizing model performance.

Training Advanced Deep Learning Models

Azure Databricks fully supports the training of deep learning models, thanks to the integration of popular frameworks like PyTorch and distributed training tools such as Horovod. This feature enables the processing of large data sets and the execution of complex AI tasks, paving the way for innovations in areas such as computer vision and natural language processing.