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Training
Designing and Implementing a Data Science Solution on Azure

Designing and Implementing a Data Science Solution on Azure

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.

Training code
DP-100
Duration
4 days
Level
Average
Price
4000 zł
Who is it for?

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.

Prerequisites

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.
Scope of training

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

Enquire about the date and quotation

This training is available on request. Let us know and we will arrange a date and format (online/in-person).

We will respond with a proposed date within 24 hours
No obligation – simply enquiring does not reserve anything
Available exclusively for your team