Exploring Azure Services in DP-100T01 Course
Are you ready to embark on a data-driven journey into the world of Azure services? Join us in the DP-100T01 Designing and Implementing a Data Science Solution on Azure course, where we dive deep into the realm of Azure services. This comprehensive program is tailored for data scientists and professionals with a passion for training and deploying machine learning models, and it’s poised to open new horizons for your career.
Unveiling the Azure Data Science Ecosystem
In this course, we unlock the secrets of Azure services. We begin by introducing you to the data science process and the pivotal role of a data scientist. Delve into the fascinating world of Azure’s data science options, and discover how Azure services can amplify and streamline your data science endeavors. Plus, you’ll become well-acquainted with Azure Notebooks, a powerful tool for data exploration and analysis.
Automation and Optimization with Azure Machine Learning
Our journey continues as we explore Azure Machine Learning service. You’ll learn to automate the entire data science process using this premier Azure service. Registering and deploying machine learning models will become second nature. Additionally, you’ll discover how AutoML and HyperDrive can work their magic in automating the more arduous aspects of the machine learning pipeline.
Managing and Monitoring for Success
No data science solution is complete without efficient management and monitoring. In this course, we equip you with the skills to automatically manage and monitor your machine learning models using the Azure Machine Learning service. By the end, you’ll be well-prepared to navigate the Azure services landscape, making informed decisions that can propel your career to new heights.
So, are you ready to unlock the potential of Azure services? Join us on this exciting educational journey, and together, we’ll take your data science expertise to the next level.
Course Details
Course Code: DP-100; Duration: 4 Days; Instructor-led
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.
Audience
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
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 ..
Methodology
This program will be conducted with interactive lectures, PowerPoint presentations, discussions and practical exercises
Course Objectives
no methodology
Outlines
Module 1: Design a data ingestion strategy for machine learning projects
Learn how to design a data ingestion solution for training data used in machine learning projects.
Lessons
- Introduction
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
- Exercise: Design a data ingestion strategy
- Knowledge check
- Summary
After completing this module, students will be able to:
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
Module 2: Design a machine learning model training solution
Learn how to design a model training solution for machine learning projects.
Lessons
- Introduction
- Identify machine learning tasks
- Choose a service to train a machine learning model
- Decide between compute options
- Exercise: Design a model training strategy
- Knowledge check
- Summary
After completing this module, students will be able to:
- Identify machine learning tasks
- Choose a service to train a model
- Choose between compute options
Module 3: Design a model deployment solution
Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.
Lessons
- Introduction
- Understand how model will be consumed
- Decide on real-time or batch deployment
- Exercise – Design a deployment solution
- Summary
After completing this module, students will be able to:
- Understand how a model will be consumed.
- Decide whether to deploy your model to a real-time or batch endpoint.
Module 4: Explore Azure Machine Learning workspace resources and assets
As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.
Lessons
- Introduction
- Create an Azure Machine Learning workspace
- Identify Azure Machine Learning resources
- Identify Azure Machine Learning assets
- Train models in the workspace
- Exercise – Explore the workspace
- Knowledge check
- Summary
After completing this module, students will be able to:
- Create an Azure Machine Learning workspace.
- Identify resources and assets.
- Train models in the workspace.
Module 5: Explore developer tools for workspace interaction
Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).
Lessons
- Introduction
- Explore the studio
- Explore the Python SDK
- Explore the CLI
- Exercise – Explore the developer tools
- Knowledge check
- Summary
After completing this module, students will be able to:
- The Python Software Development Kit (SDK).
- The Azure Command Line Interface (CLI).
Module 6: Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You’ll be introduced to datastores and data assets.
Lessons
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise – Make data available in Azure Machine Learning
- Knowledge check
- Summary
After completing this module, students will be able to:
- Work with Uniform Resource Identifiers (URIs).
- Create and use datastores.
- Create and use data assets.
Module 7: Work with compute targets in Azure Machine Learning
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Lessons
- Introduction
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Exercise – Work with compute resources
- Knowledge check
- Summary
After completing this module, students will be able to:
- Choose the appropriate compute target.
- Create and use a compute instance.
- Create and use a compute cluster.
Module 8: Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Lessons
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise – Work with environments
- Knowledge check
- Summary
After completing this module, students will be able to:
- Understand environments in Azure Machine Learning.
- Explore and use curated environments.
- Create and use custom environments.
Module 9: Find the best classification model with Automated Machine Learning
Learn how to find the best classification model with automated machine learning (AutoML). You’ll use the Python SDK (v2) to configure and run an AutoML job.
Lessons
- Introduction
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
- Exercise – Find the best classification model with Automated Machine Learning
- Knowledge check
- Summary
After completing this module, students will be able to:
- Prepare your data to use AutoML for classification.
- Configure and run an AutoML experiment.
- Evaluate and compare models.
Module 10: Track model training in Jupyter notebooks with MLflow
Learn how to use MLflow for model tracking when experimenting in notebooks.
Lessons
- Introduction
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
- Exercise – Track model training
- Knowledge check
- Summary
After completing this module, students will be able to:
- Configure to use MLflow in notebooks
- Use MLflow for model tracking in notebooks
Module 11: Run a training script as a command job in Azure Machine Learning
In this module, you will learn how to interoperate unmanaged code in your applications and how to ensure that your code releases any unmanaged resources.
Lessons
- Introduction
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Exercise – Run a training script as a command job
- Knowledge check
- Summary
After completing this module, students will be able to:
- Convert a notebook to a script.
- Test scripts in a terminal.
- Run a script as a command job.
- Use parameters in a command job.
Module 12: Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
Lessons
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise – Use MLflow to track training jobs
- Knowledge check
- Summary
After completing this module, students will be able to:
- Use MLflow when you run a script as a job.
- Review metrics, parameters, artifacts, and models from a run.
Module 13: Run pipelines in Azure Machine Learning
Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
Lessons
- Introduction
- Create components
- Create a pipeline
- Run a pipeline job
- Exercise – Run a pipeline job
- Knowledge check
- Summary
After completing this module, students will be able to:
- Create components.
- Build an Azure Machine Learning pipeline.
- Run an Azure Machine Learning pipeline.
Module 14: Perform hyperparameter tuning with Azure Machine Learning
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
Lessons
- Introduction
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Exercise – Run a sweep job
- Knowledge check
- Summary
After completing this module, students will be able to:
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Module 15: Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
Lessons
- Introduction
- Explore managed online endpoints
- Deploy your MLflow model to a managed online endpoint
- Deploy a model to a managed online endpoint
- Test managed online endpoints
- Exercise – Deploy an MLflow model to an online endpoint
- Knowledge check
- Summary
After completing this module, students will be able to:
- 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.
Module 16: Deploy a model to a batch endpointle
Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you’ll trigger a batch scoring job.
Lessons
- Introduction
- Understand and create batch endpoints
- Deploy your MLflow model to a batch endpoint
- Deploy a custom model to a batch endpoint
- Invoke and troubleshoot batch endpoints
- Exercise – Deploy an MLflow model to a batch endpoint
- Knowledge check
- Summary
After completing this module, students will be able to:
- Create a batch endpoint.
- Deploy your MLflow model to a batch endpoint.
- Deploy a custom model to a batch endpoint.
- Invoke batch endpoints.
