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DP-3014: Build machine learning solutions using Azure Databricks Training

DP-3014 covers how to build, train, and deploy machine learning models on Azure Databricks using Apache Spark MLlib. The course starts with core ML concepts and works up to model deployment, so you understand what's happening at each step rather than just following instructions.

📘 Azure 👥 148 Enrolled ⏱️ 1 Day 💼 Intermediate Level ⭐ 4.9 | 89 Reviews

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| DP-3014: Build machine learning solutions using Az

Course Overview

The DP-3014: Build machine learning solutions using Azure Databricks Training trains you to build machine learning solutions using Azure Databricks and Apache Spark. You'll prepare datasets, train models, and tune hyperparameters with MLflow and AutoML. It's structured around practical work, the kind of tasks you'd do if someone handed you a dataset and said "build a model that works in production." If you already know your way around Databricks and want to add ML to your toolkit, this is the right course.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will learn

  • Understanding Azure Databricks for Machine Learning
  • Setting Up Azure Databricks
  • Data Processing and Exploration
  • Machine Learning with Databricks
  • Building and Tuning ML Models
  • Deploying ML Models
  • Collaborative ML Development
  • Best Practices and Performance Optimization

Who Should Attend This Course?

  • Data Engineer
  • Data Scientist

Prerequisites

  • Have a experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.

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📘 DP-3014: Build machine learning solutions using Azure Databricks Outline

  • Provision an Azure Databricks workspace
  • Identify core workloads and personas for Azure Databricks
  • Describe key concepts of an Azure Databricks solution
  • Lab: Explore Azure Databricks
  • Describe key elements of the Apache Spark architecture
  • Create and configure a Spark cluster
  • Describe use cases for Spark
  • Use Spark to process and analyze data stored in files
  • Use Spark to visualize data
  • Lab: Use Spark in Azure Databricks
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model
  • Lab: Train a machine learning model in Azure Databricks
  • Use MLflow to log parameters, metrics, and other details from experiment runs
  • Use MLflow to manage and deploy trained models
  • Lab: Use MLflow in Azure Databricks
  • Use the Hyperopt library to optimize hyperparameters
  • Distribute hyperparameter tuning across multiple worker nodes
  • Lab: Optimize hyperparameters for machine learning in Azure Databricks
  • Use the AutoML user interface in Azure Databricks
  • Use the AutoML API in Azure Databricks
  • Lab: Use AutoML in Azure Databricks
  • Train a deep learning model in Azure Databricks
  • Distribute deep learning training by using the Horovod library
  • Lab: Train deep learning models on Azure Databricks
  • Automate feature engineering and data pipelines
  • Model development and training
  • Model deployment strategies
  • Model versioning and lifecycle management
  • Lab: Manage a machine learning model

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