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  • DP - 100T01 : Designing and Implementing a Data Science Solution on Azure

The "Designing and Implementing a Data Science Solution on Azure" course focuses on operating machine learning solutions at cloud scale with Azure Machine Learning. It covers managing data ingestion and preparation, model training and deployment, and monitoring machine learning solutions using Microsoft Azure. The course leverages existing knowledge of Python and machine learning to optimize the use of Azure's tools and services.

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DP - 100T01 : Designing and Implementing a Data Science Solution on Azure

Curriculum

Master DP-100T01: Designing & Implementing Data Science Solutions on Azure. Learn to build, deploy, and manage scalable AI and data science models effectively.

Ratings

( 4.6 Ratings )

Live Online Classes starting on 01 January, 1970

DP - 100T01 : Designing and Implementing a Data Science Solution on Azure

The DP-100T01: Designing and Implementing a Data Science Solution on Azure course provides an in-depth exploration of Azure's machine learning capabilities. It covers the entire data science process from data preparation, model training, model deployment, and model management. Learners will gain practical experience with Azure Machine Learning Service and Azure Machine Learning Studio, learning how to create, train, optimize, and deploy machine learning models at scale.

Throughout the course, participants will engage in hands-on labs, such as creating an Azure Machine Learning workspace, running experiments, working with datastores and datasets, and orchestrating machine learning workflows with pipelines. They will also explore real-time and batch inferencing, ensuring their models can respond promptly or handle large-scale processing. By mastering hyperparameter tuning, automated machine learning, and model interpretation, students will be well-equipped to build responsible AI solutions. They'll also delve into best practices for monitoring models to maintain optimal performance over time, using tools like Application Insights and data drift monitoring.

 

Audience Profile:

This course is aimed at data scientists who have a solid foundation in Python and experience with machine learning frameworks such as Scikit-Learn, PyTorch, and TensorFlow. The course focuses on leveraging Azure to build and manage machine learning solutions in the cloud.

 

Prerequisites:

  • Fundamental knowledge of Microsoft Azure.

  • Experience in writing Python code to work with data using libraries like NumPy, Pandas, and Matplotlib.

  • Understanding of data science, including data preparation and training machine learning models with libraries such as Scikit-Learn, PyTorch, or TensorFlow.

 

Course Outline:

Module 1: Introduction to Azure Machine Learning

  • Getting Started with Azure Machine Learning

  • Azure Machine Learning Tools

  • Lab: Creating an Azure Machine Learning Workspace

  • Lab: Working with Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

  • Training Models with Designer

  • Publishing Models with Designer

  • Lab: Creating a Training Pipeline with the Azure ML Designer

  • Lab: Deploying a Service with the Azure ML Designer

Module 3: Running Experiments and Training Models

  • Introduction to Experiments

  • Training and Registering Models

  • Lab: Running Experiments

  • Lab: Training and Registering Models

Module 4: Working with Data

  • Working with Datastores

  • Working with Datasets

  • Lab: Working with Datastores

  • Lab: Working with Datasets

Module 5: Compute Contexts

  • Working with Environments

  • Working with Compute Targets

  • Lab: Working with Environments

  • Lab: Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

  • Introduction to Pipelines

  • Publishing and Running Pipelines

  • Lab: Creating a Pipeline

  • Lab: Publishing a Pipeline

Module 7: Deploying and Consuming Models

  • Real-time Inferencing

  • Batch Inferencing

  • Lab: Creating a Real-time Inferencing Service

  • Lab: Creating a Batch Inferencing Service

Module 8: Training Optimal Models

  • Hyperparameter Tuning

  • Automated Machine Learning

  • Lab: Tuning Hyperparameters

  • Lab: Using Automated Machine Learning

Module 9: Interpreting Models

  • Introduction to Model Interpretation

  • Using Model Explainers

  • Lab: Reviewing Automated Machine Learning Explanations

  • Lab: Interpreting Models

Module 10: Monitoring Models

  • Monitoring Models with Application Insights

  • Monitoring Data Drift

  • Lab: Monitoring a Model with Application Insights

  • Lab: Monitoring Data Drift

(4.6 Ratings)

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