Kursai

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

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3 dienos lietuvių k. Vilnius 1200 EUR


Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Kursai skirti

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Kurso nauda

In this course students will learn:

  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automate Machine Learning with Azure Machine Learning service
  • Manage and Monitor Machine Learning Models with the Azure Machine Learning service

  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
  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
  3. Running Experiments and Training Models
    • Introduction to Experiments
    • Training and Registering Models
    • Lab : Running Experiments
    • Lab : Training and Registering Models
  4. Working with Data
    • Working with Datastores
    • Working with Datasets
    • Lab : Working with Datastores
    • Lab : Working with Datasets
  5. Compute Contexts
    • Working with Environments
    • Working with Compute Targets
    • Lab : Working with Environments
    • Lab : Working with Compute Targets
  6. Orchestrating Operations with Pipelines
    • Introduction to Pipelines
    • Publishing and Running Pipelines
    • Lab : Creating a Pipeline
    • Lab : Publishing a Pipeline
  7. Deploying and Consuming Models
    • Real-time Inferencing
    • Batch Inferencing
    • Lab : Creating a Real-time Inferencing Service
    • Lab : Creating a Batch Inferencing Service
  8. Training Optimal Models
    • Hyperparameter Tuning
    • Automated Machine Learning
    • Lab : Tuning Hyperparameters
    • Lab : Using Automated Machine Learning
  9. Interpreting Models
    • Introduction to Model Interpretation
    • using Model Explainers
    • Lab : Reviewing Automated Machine Learning Explanations
    • Lab : Interpreting Models
  10. Monitoring Models
    • Monitoring Models with Application Insights
    • Monitoring Data Drift
    • Lab : Monitoring a Model with Application Insights
    • Lab : Monitoring Data Drift

Before attending this course, students must have:

  • A fundamental knowledge of Microsoft Azure
  • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
  • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

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