DP-100T01 - 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 with Azure Machine Learning and MLflow.

AED 2250

Date : 25 Mar 2024

Code: dp-100t01

Duration: 4.0 days

other dates

Schedule

Virtual ILT | 25 Mar 2024 - 27 Mar 2024
Virtual ILT | 01 Apr 2024 - 03 Apr 2024
Virtual ILT | 13 May 2024 - 15 May 2024
Virtual ILT | 10 Jun 2024 - 12 Jun 2024
Virtual ILT | 08 Jul 2024 - 10 Jul 2024
Virtual ILT | 12 Aug 2024 - 14 Aug 2024
Virtual ILT | 09 Sep 2024 - 11 Sep 2024
Virtual ILT | 07 Oct 2024 - 09 Oct 2024
Virtual ILT | 11 Nov 2024 - 13 Nov 2024
Virtual ILT | 09 Dec 2024 - 11 Dec 2024

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Objectives

  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion solution
  • Identify machine learning tasks
  • Choose a service to train a model
  • Choose between computing options
  • Understand how a model will be consumed.
  • Decide whether to deploy your model to a real-time or batch endpoint.
  • Create an Azure Machine Learning workspace.
  • Identify resources and assets.
  • Train models in the workspace.
  • The Azure Machine Learning studio.
  • The Python Software Development Kit (SDK).
  • The Azure Command Line Interface (CLI).
  • Work with Uniform Resource Identifiers (URIs).
  • Create and use datastores.
  • Create and use data assets.
  • Choose the appropriate compute target.
  • Create and use a compute instance.
  • Create and use a compute cluster.
  • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.
  • Prepare your data to use AutoML for classification.
  • Configure and run an AutoML experiment.
  • Evaluate and compare models.
  • Configure to use MLflow in notebooks
  • Use MLflow for model tracking in notebooks
  • Convert a notebook to a script.
  • Test scripts in a terminal.
  • Run a script as a command job.
  • Use parameters in a command job.
  • Use MLflow when you run a script as a job.
  • Review metrics, parameters, artifacts, and models from a run.
  • Create components.
  • Build an Azure Machine Learning pipeline.
  • Run an Azure Machine Learning pipeline.
  • Define a hyperparameter search space.
  • Configure hyperparameter sampling.
  • Select an early-termination policy.
  • Run a sweep job.
  • Use managed online endpoints.
  • Deploy your MLflow model to a managed online endpoint.
  • Deploy a custom model to a managed online endpoint.
  • Test online endpoints.
  • Create a batch endpoint.
  • Deploy your MLflow model to a batch endpoint.
  • Deploy a custom model to a batch endpoint.
  • Invoke batch endpoints.

Content

1. Design a machine learning solution

There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.

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2. Explore the Azure Machine Learning workspace

Throughout this learning path you'll explore the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. You'll also explore the various developer tools you can use to interact with the workspace.

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3. Work with data in Azure Machine Learning

Learn how to work with data in Azure Machine Learning. Whether you want to access data in notebooks or scripts, you can read data directly, through datastores, or data assets.

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4. Work with compute in Azure Machine Learning

Learn how to work with compute targets and environments in the Azure Machine Learning workspace.

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5. Automate machine learning model selection with Azure Machine Learning

Learn how to find the best model with automated machine learning (AutoML). Whether you're training a classification, regression, or forecasting model, you can use AutoML to quickly explore various featurization techniques and algorithms.

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6. Use notebooks for experimentation in Azure Machine Learning

Learn how to use Azure Machine Learning notebooks for experimentation. Similar to Jupyter, the notebooks are ideal for exploring your data and developing a machine learning model.

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7. Train models with scripts in Azure Machine Learning

To prepare your machine learning workloads for production, you'll work with scripts. Learn how to train models with scripts in Azure Machine Learning.

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8. Optimize model training with pipelines in Azure Machine Learning

Learn how to optimize and automate model training in Azure Machine Learning by using components and pipelines.

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9. Manage and review models in Azure Machine Learning

Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.

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10. Deploy and consume models with Azure Machine Learning

Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.

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Audience

DP-100T01 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

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:
  • Creating cloud resources in Microsoft Azure. 
  • Using Python to explore and visualize data. 
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. 
  • Working with containers
To gain these prerequisite skills, take the following free online training before attending the course:
  • Explore Microsoft cloud concepts. 
  • Create machine learning models. Administer containers in Azure
  • If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

Certification

product-certification

Skills Measured

  • Design and prepare a machine learning solution (20–25%)
  • Explore data and train models (35–40%)
  • Prepare a model for deployment (20–25%)
  • Deploy and retrain a model (10–15%)

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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