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


What DP-100T01: Designing and Implementing a Data Science Solution on Azure training is all about?

DP-100T01: Designing and Implementing a Data Science Solution on Azure Training teaches the techniques to work with machine learning solutions at the cloud scale with the help of Azure Machine Learning. This technical course enhances and supports one’s current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure. Our enterprise training program is best for organizations and companies. It teaches automation, management, and monitoring of machine learning using Azure Machine Learning service. This training program is ideal for data scientists with experience in machine learning frameworks and Python, who want to develop and work with machine learning solutions in the cloud. It is also very useful for individuals preparing for the Microsoft Certified: Azure Data Scientist Associate certification exam.

This training is designed based on the objectives of the course variant DP-100T01-A

Schedule
  • Delivery Format:
Date: Nov 18, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Nov 25, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Dec 02, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Dec 08, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Dec 16, 2020 | 9:00 am - 5:00 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Dec 21, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
  • Delivery Format:
Date: Dec 28, 2020 | 9:00 am - 5:30 pm EST
Location: Online
$1725 USD
What are the course objectives for DP-100T01: Designing and Implementing a Data Science Solution on Azure training?
  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automating Machine Learning with Azure Machine Learning service
  • Managing and Monitoring Machine Learning Models with the Azure Machine Learning service
Who should attend DP-100T01: Designing and Implementing a Data Science Solution on Azure training?

Data scientists who know Python and existing understanding of machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn who want to create and manage machine learning solutions in the cloud are the primary audiences for this training.

What is the course outline for DP-100T01: Designing and Implementing a Data Science Solution on Azure training?
  • 1. Introduction to Azure Machine Learning
  • In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools
  • Lab: Creating an Azure Machine Learning Workspace

    Lab: Working with Azure Machine Learning Tools

    After completing this module, you will be able to

  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning
  • 2. No-Code Machine Learning with Designer
  • This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

  • 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

    After completing this module, you will be able to

  • Use designer to train a machine learning model
  • Deploy a Designer pipeline as a service
  • 3. Running Experiments and Training Models
  • In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

  • Introduction to Experiments
  • Training and Registering Models
  • Lab: Running Experiments

    Lab: Training and Registering Models

    After completing this module, you will be able to

  • Run code-based experiments in an Azure Machine Learning workspace
  • Train and register machine learning models
  • 4. Working with Data
  • Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

  • Working with Datastores
  • Working with Datasets
  • Lab: Working with Datastores

    Lab: Working with Datasets

    After completing this module, you will be able to

  • Create and consume datastores
  • Create and consume datasets
  • 5. Compute Contexts
  • One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

  • Working with Environments
  • Working with Compute Targets
  • Lab: Working with Environments

    Lab: Working with Compute Targets

    After completing this module, you will be able to

  • Create and use environments
  • Create and use compute targets
  • 6. Orchestrating Operations with Pipelines
  • Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.

  • Introduction to Pipelines
  • Publishing and Running Pipelines
  • Lab: Creating a Pipeline

    Lab: Publishing a Pipeline

    After completing this module, you will be able to

  • Create pipelines to automate machine learning workflows
  • Publish and run pipeline services
  • 7. Deploying and Consuming Models
  • Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

  • Real-time Inferencing
  • Batch Inferencing
  • Lab: Creating a Real-time Inferencing Service

    Lab: Creating a Batch Inferencing Service

    After completing this module, you will be able to

  • Publish a model as a real-time inference service
  • Publish a model as a batch inference service
  • 8. Training Optimal Models
  • By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

  • Hyperparameter Tuning
  • Automated Machine Learning
  • Lab: Tuning Hyperparameters

    Lab: Using Automated Machine Learning

    After completing this module, you will be able to

  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data
  • 9. Interpreting Models
  • Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

  • Introduction to Model Interpretation
  • using Model Explainers
  • Lab: Reviewing Automated Machine Learning Explanations

    Lab: Interpreting Models

    After completing this module, you will be able to

  • Generate model explanations with automated machine learning
  • Use explainers to interpret machine learning models
  • 10. Monitoring Models
  • After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

  • Monitoring Models with Application Insights
  • Monitoring Data Drift
  • Lab: Monitoring a Model with Application Insights

    Lab: Monitoring Data Drift

    After completing this module, you will be able to

  • Use Application Insights to monitor a published model
  • Monitor data drift
3 Days | $ 1725
4.7
  279 Ratings

1478 Learners

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