Implementing a Machine Learning solution with Azure Databricks (DP-3014) Training in France

  • Learn via: Classroom
  • Duration: 1 Day
  • Level: Intermediate
  • Price: From €1,248+VAT

Azure Databricks is a cloud-scale platform for data analytics and machine learning. This course equips data scientists and machine learning engineers with the skills to implement scalable data processing and machine learning solutions using Azure Databricks. Participants will explore key concepts, including Apache Spark for data analytics, model training and tuning, AutoML, and MLflow for experiment tracking. The course emphasises data transformation, feature engineering, and model deployment. Through hands-on labs, learners will gain practical experience in building, training, and deploying models in a collaborative environment.

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We can host this training at your preferred location.

Prerequisites

Participants should have:

  • Experience using Python to explore data and train machine learning models.
  • Familiarity with common open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
  • Recommended: Completion of the Create machine learning models learning path before starting this course.

Target audience

This course is designed for:

  • Data scientists and machine learning engineers looking to scale their ML solutions.
  • AI and data professionals working with large-scale data analytics and processing.
  • Teams responsible for implementing machine learning workflows in Azure.
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What You Will Learn

By the end of this course, learners will be able to:

  • Describe the capabilities of Azure Databricks and its role in both data analytics and machine learning.
  • Use Apache Spark in Azure Databricks to process, transform, and analyse large-scale data.
  • Train and evaluate machine learning models within Azure Databricks.
  • Leverage MLflow for experiment tracking, model registration, and deployment.
  • Optimise hyperparameters using Hyperopt and review trial results.
  • Implement AutoML using both the Azure Databricks user interface and code-based approaches.
  • Manage machine learning workflows in production, including data automation, versioning, and deployment strategies.
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Outline

Explore Azure Databricks

  • Overview of Azure Databricks as a cloud-scale platform for data analytics and machine learning
  • Key concepts and workloads in Azure Databricks
  • Data governance using Unity Catalog and Microsoft Purview
  • Hands-on exercise: Exploring Azure Databricks

Use Apache Spark in Azure Databricks

  • Introduction to Apache Spark and its role in large-scale data analytics
  • Creating and managing Spark clusters
  • Using Spark notebooks to process and transform large datasets
  • Working with structured and unstructured data files in Spark
  • Visualising data using Spark and Databricks
  • Hands-on exercise: Using Spark for data analytics

Train a machine learning model in Azure Databricks

  • Principles of machine learning and predictive modelling
  • Preparing data for machine learning, including feature engineering and transformations
  • Training machine learning models using Scikit-Learn, PyTorch, and TensorFlow
  • Evaluating model performance using standard machine learning metrics
  • Hands-on exercise: Training a machine learning model

Use MLflow in Azure Databricks

  • Introduction to MLflow and its role in the machine learning lifecycle
  • Running experiments and tracking performance metrics with MLflow
  • Registering, serving, and managing models using MLflow
  • Deploying trained models for inference within Databricks
  • Hands-on exercise: Using MLflow for model management

Tune hyperparameters in Azure Databricks

  • Understanding hyperparameter tuning and its impact on machine learning models
  • Using Hyperopt for automated hyperparameter tuning in Azure Databricks
  • Reviewing and analysing Hyperopt trials for optimisation insights
  • Scaling Hyperopt trials for improved performance
  • Hands-on exercise: Tuning model hyperparameters using Hyperopt

Use AutoML in Azure Databricks

  • Overview of AutoML and its benefits in machine learning
  • Running AutoML experiments via the Azure Databricks user interface
  • Using Python code to execute AutoML workflows
  • Comparing AutoML results with traditional model development
  • Hands-on exercise: Using AutoML for machine learning model development

Train deep learning models in Azure Databricks

  • Fundamentals of deep learning and neural networks
  • Training deep learning models using PyTorch in Databricks
  • Using TorchDistributor for distributed deep learning model training
  • Deploying deep learning models for real-world AI tasks
  • Hands-on exercise: Training and optimising deep learning models in Databricks

Manage machine learning in production with Azure Databricks

  • Automating data transformations and machine learning workflows in Databricks
  • Exploring model development, versioning, and lifecycle management
  • Deploying models for real-time inference and decision-making
  • Monitoring deployed models for performance and drift detection
  • Hands-on exercise: Managing a machine learning model in production

Exams and assessments

This course does not include formal exams. Participants will complete interactive labs and knowledge checks to reinforce learning outcomes.

Hands-on learning

This course includes:

  • Hands-on labs for data processing, model training, hyperparameter tuning, and model deployment.
  • Practical exercises using Apache Spark, MLflow, AutoML, and Hyperopt.
  • Real-world case studies on implementing scalable machine learning solutions.
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Contact us for more detail about our trainings and for all other enquiries!

Avaible Training Dates

Join our public courses in our France facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

04 avril 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
12 avril 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
19 avril 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
23 avril 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
27 avril 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
14 mai 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
14 mai 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
06 juin 2025 (1 Day)
Paris, Lyon, Lille, Nice
Classroom / Virtual Classroom
€1,248 +VAT
Implementing a Machine Learning solution with Azure Databricks (DP-3014) Training Course in France

The French Republic, or République Française, is a country of northwestern Europe. Having suitable agricultural lands, more than half of the land, makes France Europe's most important agricultural producer. The country's capital and the largest city is Paris. Other major and famous cities are Marseille, Lyon, Toulouse, Lille, Bordeaux, and Nice.

France is a popular destination for tourists. While you can skii at the Alps, Côte d'Azur is a perfect destination in spring and summer time. The country attracts millions of tourists also during the Cannes Festival time. The Cannes Film Festival, is an annual film festival held in Cannes, a city located on the French Riviera, which previews new films of all genres from all around the world.

Bilginç IT Academy is committed to aligning with the demands of France, utilizing state-of-the-art training methodologies. Discover a wide selection of Certification Exam preparation courses and accredited corporate training offerings in our catalog, designed to reshape your perspective on IT training.
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