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Train and deploy a machine learning model with Azure Machine Learning Course Overview

The DP-3007 course equips professionals with the skills to train and deploy machine learning models using Azure Machine Learning. This vital training is designed for data scientists, AI engineers, and business analysts, enabling them to harness cloud-based tools to enhance decision-making and drive innovation in their organizations. Unlock your potential in the rapidly evolving field of machine learning.


Course Introduction

Price:

USD 750

Duration:

8 Hrs.

  • Introduction to Azure Machine Learning
    • Overview of Azure Machine Learning capabilities
    • Understanding machine learning concepts and workflows
  • Data Preparation and Exploration
    • Data ingestion techniques
    • Data cleaning and preprocessing
    • Exploratory data analysis
  • Building Machine Learning Models
    • Selecting appropriate algorithms
    • Feature engineering and selection
    • Training models with Azure Machine Learning
  • Model Evaluation and Tuning
    • Evaluating model performance metrics
    • Hyperparameter tuning and optimization techniques
    • Cross-validation methods
  • Deployment of Machine Learning Models
    • Understanding deployment options in Azure
    • Creating and managing endpoints for model deployment
    • Containerization of models using Docker
  • Monitoring and Managing Deployed Models
    • Implementing model monitoring techniques
    • Managing model versions and updates
    • Retraining models based on new data
  • Integrating Machine Learning with Azure Services
    • Leveraging Azure Data Services for data management
    • Using Azure Functions and Logic Apps for automation
    • Exploring integration with Power BI for visualization
  • Best Practices and Governance in Machine Learning
    • Understanding ethical AI and responsible machine learning
    • Implementing security and compliance measures
    • Documentation and model governance strategies
  • Capstone Project
    • Hands-on project to apply learned skills
    • End-to-end machine learning solution development
    • Presentation and evaluation of project outcomes

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