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


Course Description

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.

Course Objectives

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 in Microsoft Azure.

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 solution

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 solution

  • Understand how model will be consumed
  • Decide on real-time or batch deployment

4 - Explore Azure Machine Learning workspace resources and assets

  • Video - Explore the Azure Machine Learning workspace
  • Create an Azure Machine Learning workspace
  • Identify Azure Machine Learning resources
  • Identify Azure Machine Learning assets
  • Train models in the workspace

5 - Explore developer tools for workspace interaction

  • Explore the studio
  • Explore the Python SDK
  • Explore the CLI

6 - Make data available in Azure Machine Learning

  • Video - 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

  • Choose the appropriate compute target
  • 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

  • Video - 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

  • Video - 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

  • Video - Track model training with MLFlow in jobs
  • Track metrics with MLflow
  • View metrics and evaluate models

13 - Run pipelines in Azure Machine Learning

  • Video - Run pipelines in Azure Machine Learning
  • Create components
  • Create a pipeline
  • Run a pipeline job

14 - Perform hyperparameter tuning with Azure Machine Learning

  • Video - 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

  • Video - 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

  • Video - 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

Course Prerequisites

  • 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
  • AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.

Course Information

Length: 4 day

Format: Lecture and Lab

Delivery Method: Virtual

Max. Capacity: 16



Schedule

Contact Us

UPCOMING COURSES
Date
Geography & Location
Days
Cost
CLC
GTR
Mar 11, 2024 - 4 day(s)
Mar 11, 2024
AMER
Remote EST
AMER, Remote EST
4
$2380 USD
$2380 USD
May 28, 2024 - 4 day(s)
May 28, 2024
AMER
Remote EST
AMER, Remote EST
4
$2380 USD
$2380 USD

Do you have more questions? We're delighted to assist you!

1-877-797-2799
info@firefly.cloud

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.