DP-100

DP-100 - Designing and Implementing Data Science Solution on Azure

Course Duration: 4 Days

Delivery Methods: Classroom Training - Instructor Led or Online


COURSE OVERVIEW

The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.


WHO SHOULD ATTEND?

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.


COURSE PREREQUISITES

Recommended Prerequisite course:

AI-900 Microsoft Azure AI Fundamentals

  • 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

Course Outline

1 - Design a data ingestion strategy for machine learning projects
  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion sulution
2 - Design a machine learning model training solution
  • Identify machine learning tasks
  • Choose a service to train a machine learning model
  • Decide between compute options
3 - Design a model deployment sulution
  • Understand how model will be consumed
  • Decide on real-time or batch deployment
4 - Explore Azure Machine Learning workspace resources and assets
  • Create an Azure Machine Learning workspace
  • Identify Azure Machine Learning resources
  • Identify Azure Machine Learning assets
  • Train models in the workspace
5 - Explore developer touls for workspace interaction
  • Explore the studio
  • Explore the Python SDK
  • Explore the CLI
6 - Make data available in Azure Machine Learning
  • Understand URIs
  • Create a datastore
  • Create a data asset
7 - Work with compute targets in Azure Machine Learning
  • Create and use a compute instance
  • Create and use a compute instance
  • Create and use a compute cluster
8 - Work with environments in Azure Machine Learning
  • Understand environments
  • Explore and use curated environments
  • Create and use custom environments
9 - Find the best classification model with Automated Machine Learning
  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models
10 - Track model training in Jupyter notebooks with MLflow
  • Configure MLflow for model tracking in notebooks
  • Train and track models in notebooks
11 - Run a training script as a command job in Azure Machine Learning
  • Convert a notebook to a script
  • Run a script as a command job
  • Use parameters in a command job
12 -Track model training with MLflow in jobs
  • Track metrics with MLflow
  • View metrics and evaluate models
13 - Run pipelines in Azure Machine Learning
  • Create components
  • Create a pipeline
  • Run a pipeline job
14 - Perform hyperparameter tuning with Azure Machine Learning
  • Define a search space
  • Configure a sampling method
  • Configure early termination
  • Use a sweep job for hyperparameter tuning
15 - Deploy a model to a managed online endpoint
  • 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
16 - Deploy a model to a batch endpoint
  • 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
Dates & Times

* Actual dates may vary.