DP-3014: Implementing a Machine Learning solution with Azure Databricks

  • Duration: 1 Day (8 Hours)
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DP-3014: Implementing a Machine Learning solution with Azure Databricks Course Overview

Master in-demand machine learning skills with DP-3014: craft impactful solutions using Azure Databricks, explore model building with frameworks like TensorFlow & PyTorch, optimize performance with hyperparameter tuning, & gain hands-on experience through labs. Perfect for data scientists, developers, & AI enthusiasts – enroll now & become a machine learning whiz!

Intended Audience

  • Data Scientists
  • Data Engineers
  • Data Analysts
  • Machine Learning Engineers
  • AI Developers
  • Software Developers
  • Cloud Solution Architects
  • IT Managers and Decision Makers
  • Business Intelligence Developers
  • Anyone interested in learning about implementing machine learning solutions using Azure Databricks.

Learning Objectives of DP-3014: Implementing a Machine Learning solution with Azure Databricks

  • Master Azure Databricks & Apache Spark architecture.
  • Manage workspaces & clusters in Databricks.
  • Utilize data storage options like Data Lake Storage, SQL Data Warehouse & Cosmos DB.
  • Preprocess & clean data for machine learning models.
  • Train & evaluate models for classification, regression, & clustering.
  • Leverage AutoML for hyperparameter tuning.
  • Deploy & manage models in production environments.
  • Monitor & debug machine learning pipelines.
  • Apply supervised & unsupervised learning techniques.
  • Understand common machine learning algorithms & applications.
  • Utilize Spark MLlib, TensorFlow, & PyTorch for model development.
  • Perform feature engineering & dimensionality reduction.
  • Implement data splitting, cross-validation, & evaluation techniques.
  • Select & tune hyperparameters for optimal model performance.
  • Interpret model results & explainability.
  • Deploy models as services using MLflow & Databricks Model Serving.
  • Integrate models with web applications & other systems.
  • Monitor & diagnose model performance in production.
  • Understand the business value of machine learning & its applications.
  • Learn best practices for building & deploying machine learning solutions.
  • Prepare for data scientist & machine learning engineer roles in the cloud.
Explore Azure Databricks
  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Describe key concepts of an Azure Databricks solution.
  • 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.
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model
  • Use MLflow to log parameters, metrics, and other details from experiment runs.
  • Use MLflow to manage and deploy trained models.
  • Use the Hyperopt library to optimize hyperparameters.
  • Distribute hyperparameter tuning across multiple worker nodes.
  • Use the AutoML user interface in Azure Databricks
  • Use the AutoML API in Azure Databricks
  • Train a deep learning model in Azure Databricks
  • Distribute deep learning training by using the Horovod library

DP-3014: Implementing a Machine Learning solution with Azure Databricks Course Prerequisites

  • Fundamental knowledge of machine learning concepts and techniques.
  • Proficiency in a programming language such as Python or Scala, including experience with data manipulation libraries (e.g., Pandas, NumPy).
  • Familiarity with data analysis and data visualization tools and techniques.
  • Basic understanding of cloud computing concepts and Azure services.
  • Prior experience with Azure Databricks or Apache Spark is beneficial but not mandatory.
  • Understanding of statistics and probability concepts used in machine learning algorithms.
  • Familiarity with common machine learning algorithms and techniques for supervised and unsupervised learning.
  • Knowledge of SQL for data querying and manipulation.
  • Understanding of data preprocessing and feature engineering techniques.
  • Familiarity with model evaluation and performance metrics in machine learning.

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