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

Ref. DP-3014
Duration:
1
 day
Exam:
Optional
Level:
Intermediate

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

Module 2: Use Apache Spark in Azure Databricks

  • Get to know 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 in Azure Databricks

  • Understand principles of machine learning
  • Machine learning in Azure Databricks
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model

Module 4: Use MLflow in Azure Databricks

  • Capabilities of MLflow
  • 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 user interface
  • 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 Horovod

Documentation

  • Digital course material included

Complementary courses

Temptraining funding

ITTA is a partner of Temptraining, the continuing education fund for temporary workers. This training fund can subsidize continuing education for anyone who works for an employer subject to the Collective Work Agreement (CCT) Rental of services.

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.

Prix de l'inscription
CHF 850.-
Inclus dans ce cours
  • Training provided by a certified trainer
  • 180 days of access to Official Microsoft Labs
  • Official documentation in digital format
  • Official Microsoft achievement badge

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