Description

Duration

4 Days

24 CPD hours

This course is intended for

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.

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 in Microsoft Azure.

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.

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 - Design a machine learning operations solution

Explore an MLOps architecture

Design for monitoring

Design for retraining

5 - 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

6 - Explore developer tools for workspace interaction

Explore the studio

Explore the Python SDK

Explore the CLI

7 - Make data available in Azure Machine Learning

Understand URIs

Create a datastore

Create a data asset

8 - 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

9 - Work with environments in Azure Machine Learning

Understand environments

Explore and use curated environments

Create and use custom environments

10 - Find the best classification model with Automated Machine Learning

Preprocess data and configure featurization

Run an Automated Machine Learning experiment

Evaluate and compare models

11 - Track model training in Jupyter notebooks with MLflow

Configure MLflow for model tracking in notebooks

Train and track models in notebooks

12 - 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

13 - Track model training with MLflow in jobs

Track metrics with MLflow

View metrics and evaluate models

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 - Run pipelines in Azure Machine Learning

Create components

Create a pipeline

Run a pipeline job

16 - Register an MLflow model in Azure Machine Learning

Log models with MLflow

Understand the MLflow model format

17 - 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

18 - 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

19 - 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 Images

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

By Nexus Human

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