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

Price
$2,495.00 USD

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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.

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.

  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

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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.

Agenda

1 - 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

2 - Explore developer tools for workspace interaction

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

3 - Make data available in Azure Machine Learning

  • Understand URIs
  • Create a datastore
  • Create a data asset

4 - 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

5 - Work with environments in Azure Machine Learning

  • Understand environments
  • Explore and use curated environments
  • Create and use custom environments

6 - Find the best classification model with Automated Machine Learning

  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models

7 - Track model training in Jupyter notebooks with MLflow

  • Configure MLflow for model tracking in notebooks
  • Train and track models in notebooks

8 - 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

9 - Track model training with MLflow in jobs

  • Track metrics with MLflow
  • View metrics and evaluate models

10 - 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

11 - Run pipelines in Azure Machine Learning

  • Create components
  • Create a pipeline
  • Run a pipeline job

12 - Register an MLflow model in Azure Machine Learning

  • Log models with MLflow
  • Understand the MLflow model format
  • Register an MLflow model

13 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

  • Understand Responsible AI
  • Create the Responsible AI dashboard
  • Evaluate the Responsible AI dashboard

14 - 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

15 - 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

16 - Introduction to Azure AI Foundry

  • What is Azure AI Foundry?
  • How does Azure AI Foundry work
  • When to use Azure AI Foundry

17 - Explore and deploy models from the model catalog in Azure AI Foundry portal

  • Explore the language models in the model catalog
  • Deploy a model to an endpoint
  • Improve the performance of a language model

18 - Get started with prompt flow to develop language model apps in the Azure AI Foundry

  • Understand the development lifecycle of a large language model (LLM) app
  • Understand core components and explore flow types
  • Explore connections and runtimes
  • Explore variants and monitoring options

19 - Build a RAG-based agent with your own data using Azure AI Foundry

  • Understand how to ground your language model
  • Make your data searchable
  • Build an agent with prompt flow

20 - Fine-tune a language model with Azure AI Foundry

  • Understand when to fine-tune a language model
  • Prepare your data to fine-tune a chat completion model
  • Explore fine-tuning language models in Azure AI Studio

21 - Evaluate the performance of generative AI apps with Azure AI Foundry

  • Assess the model performance
  • Manually evaluate the performance of a model
  • Assess the performance of your generative AI apps

22 - Responsible generative AI

  • Plan a responsible generative AI solution
  • Identify potential harms
  • Measure potential harms
  • Mitigate potential harms
  • Operate a responsible generative AI solution
 

Upcoming Class Dates and Times

Oct 13, 14, 15, 16
8:00 AM - 4:00 PM
ENROLL $2,495.00 USD
Weekday Sessions / Guaranteed to Run
Virtual: Online - CST
CourseID: 3601718E
               
 



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