Designing and Implementing a Data Science Solution on Azure(DP-100T01-A)

4.5/5

Designing and Implementing a Data Science Solution on Azure course will give you the knowledge of data science and machine learning to implement and run workloads on Azure using Azure Machine Learning Service. In 3 days of this course you will get the 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. It prepares the Data Engineers for the data to be used for the models in which the Data Scientist determines what data is needed for model training, creates model features from the data, determines what machine learning model to use, trains and evaluates the model, and often has involvement in model deployment. The course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models. After successfully completing the course you will be given the DP-100T01: Designing and Implementing a Data Science Solution on Azure certificate.

Training Options

Classroom Training

Online Instructor Led

Onsite Training

Course Information

Module 1: Getting Started with 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.

Lessons

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning

Lab : Create an Azure Machine Learning Workspace

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

Module 2: No-Code Machine Learning

This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

Lessons

  • Automated Machine Learning
  • Azure Machine Learning Designer

Lab : Use Automated Machine Learning

Lab : Use Azure Machine Learning Designer

After completing this module, you will be able to

  • Use automated machine learning to train a machine learning model
  • Use Azure Machine Learning designer to train a model

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Module 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.

Lessons

  • Introduction to Experiments
  • Training and Registering Models

Lab : Run Experiments

Lab : Train 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

Module 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.

Lessons

  • Working with Datastores
  • Working with Datasets

Lab : Work with Data

After completing this module, you will be able to

  • Create and use datastores
  • Create and use datasets

Module 5: Working with Compute

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.

Lessons

  • Working with Environments
  • Working with Compute Targets

Lab : Work with Compute

After completing this module, you will be able to

  • Create and use environments
  • Create and use compute targets

Module 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.

Lessons

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Lab : Create a Pipeline

After completing this module, you will be able to

  • Create pipelines to automate machine learning workflows
  • Publish and run pipeline services

Module 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.

Lessons

  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery

Lab : Create a Real-time Inferencing Service

Lab : Create 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
  • Describe techniques to implement continuous integration and delivery

Module 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.

Lessons

  • Hyperparameter Tuning
  • Automated Machine Learning

Lab : Tune Hyperparameters

Lab : Use Automated Machine Learning from the SDK

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

Module 9: Responsible Machine Learning

Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.

Lessons

  • Differential Privacy
  • Model Interpretability
  • Fairness

Lab : Explore Differential privacy

Lab : Interpret Models

Lab : Detect and Mitigate Unfairness

After completing this module, you will be able to

  • Apply differential privacy to data analysis
  • Use explainers to interpret machine learning models
  • Evaluate models for fairness

Module 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.

Lessons

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Lab : Monitor a Model with Application Insights

Lab : Monitor Data Drift

After completing this module, you will be able to

  • Use Application Insights to monitor a published model
  • Monitor data drift

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Audience Profile

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.

Q: What will be the course objectives of DP-100T01: Designing and Implementing a Data Science Solution on Azure?

A: The purpose of this course is to develop, train, and deploy machine learning solutions that support data science. It doesn’t teach students about data sciences but they know that the students enrolling for this course already have a knowledge of data sciences. 

Q: What are the some key points that are required before taking DP-100T01: Designing and Implementing a Data Science Solution on Azure course?

A: Some of the major key requirements before taking the course is to have experience of writing Python code to work with data. 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.

Q: What will you learn by taking  DP-100T01: Designing and Implementing a Data Science Solution on Azure course?

A: By opting this course of Designing and Implementing a Data Science Solution you will get the information about doing Data Science on Azure and Azure Machine Learning service, Automate Machine Learning, Manage and Monitor Machine Learning Models with the Azure Machine Learning service.

Q: Skills that are required for DP-100T01: Designing and Implementing a Data Science Solution on Azure course?

A: Setting up an Azure Machine Learning workspace, Running experiments and train models, Optimizing and managing models, Deploying and consuming models.

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    What People say?

    Mohammed Aljbreen Operation Specialist, SAMA

    The Clarity of the Content was very good. The explanation of the trainer with in-depth knowledge in a proper flow really impressed me to give 5 star rating.

    Arindam Chakraborty Systems Specialist, King Abdullah University of Sciences & Technology

    The Instructor was really impressive. Clear cut explanation of every topic he covered with real time scenarios.

    Sher Afzal Khan Cloud Engineer, Cloud 9 Networks

    The Trainer and the Course Material, both are good. Good flow of explanation with simple examples. The complete training was focused on current industry challenges.

    Jawed Ahmad Siddiqui Sr. System Administrator, Saudi Ceramics

    The Trainer’s presentation was impressed me to continue the course till end. Never feel bore till the entire sessions. She studied our mindset and follows.

      Mohammed Aljbreen Operation Specialist, SAMA

      The Clarity of the Content was very good. The explanation of the trainer with in-depth knowledge in a proper flow really impressed me to give 5 star rating.

      Arindam Chakraborty Systems Specialist, King Abdullah University of Sciences & Technology

      The Instructor was really impressive. Clear cut explanation of every topic he covered with real time scenarios.

      Sher Afzal Khan Cloud Engineer, Cloud 9 Networks

      The Trainer and the Course Material, both are good. Good flow of explanation with simple examples. The complete training was focused on current industry challenges.

      Jawed Ahmad Siddiqui Sr. System Administrator, Saudi Ceramics

      The Trainer’s presentation was impressed me to continue the course till end. Never feel bore till the entire sessions. She studied our mindset and follows.

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