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Implementing a Machine Learning Solution with Microsoft Azure Databricks Course Overview

The DP-090T00 course equips participants with essential skills to implement machine learning solutions using Microsoft Azure Databricks. This course is crucial for data scientists, machine learning engineers, and IT professionals, empowering them to harness big data analytics and streamline data workflows. Enhance your expertise in leveraging Azure Databricks to drive impactful machine learning projects.


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Course Introduction

Price:

USD 700

Duration:

8 Hrs.

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  • Introduction to Azure Databricks
    • Overview of Databricks and its ecosystem
    • Understanding Apache Spark architecture
    • Configuring Azure Databricks workspace
  • Data Preparation and Exploration
    • Importing and cleaning data using Spark DataFrames
    • Data transformation techniques
    • Exploratory data analysis with visualizations
  • Feature Engineering
    • Identifying and creating features for machine learning
    • Scaling and normalizing data
    • Handling categorical variables
  • Machine Learning with Azure Databricks
    • Introduction to Machine Learning concepts
    • Using MLlib for model training
    • Building and evaluating machine learning models
  • Hyperparameter Tuning and Model Optimization
    • Techniques for hyperparameter tuning
    • Cross-validation strategies
    • Model selection and comparison
  • Deploying Machine Learning Models
    • Preparing models for deployment
    • Using MLflow for tracking experiments and models
    • Deploying models as REST APIs
  • Integrating with Azure Services
    • Working with Azure Synapse Analytics
    • Using Azure Blob Storage for data storage
    • Leveraging Azure Machine Learning for advanced scenarios
  • Monitoring and Managing Models
    • Setting up monitoring for deployed models
    • Retraining and updating models based on performance
    • Best practices for model maintenance
  • Case Studies and Practical Applications
    • Real-world applications of machine learning with Azure Databricks
    • Hands-on projects and use cases
    • Collaboration and sharing results within Databricks notebooks

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