Course Summary

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.Students will learn to,

Design a machine learning solution
Explore the Azure Machine Learning workspace
Work with data in Azure Machine Learning
Work with compute in Azure Machine Learning
Automate machine learning model selection with Azure Machine Learning
Use notebooks for experimentation in Azure Machine Learning
Train models with scripts in Azure Machine Learning
Optimize model training with pipelines in Azure Machine Learning
Manage and review models in Azure Machine Learning
Deploy and consume models with Azure Machine Learning

Design and prepare a machine learning solution (20–25%)
Design a machine learning solution
Determine the appropriate compute specifications for a training workload

Describe model deployment requirements

Select which development approach to use to build or train a model

Manage an Azure Machine Learning workspace
Create an Azure Machine Learning workspace

Manage a workspace by using developer tools for workspace interaction

Set up Git integration for source control

Create and manage registries

Manage data in an Azure Machine Learning workspace
Select Azure Storage resources

Register and maintain datastores

Create and manage data assets

Manage compute for experiments in Azure Machine Learning
Create compute targets for experiments and training

Select an environment for a machine learning use case

Configure attached compute resources, including Apache Spark pools

Monitor compute utilization

Explore data and train models (35–40%)
Explore data by using data assets and data stores
Access and wrangle data during interactive development

Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer
Create a training pipeline

Consume data assets from the designer

Use custom code components in designer

Evaluate the model, including responsible AI guidelines

Use automated machine learning to explore optimal models
Use automated machine learning for tabular data

Use automated machine learning for computer vision

Use automated machine learning for natural language processing

Select and understand training options, including preprocessing and algorithms

Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training
Develop code by using a compute instance

Track model training by using MLflow

Evaluate a model

Train a model by using Python SDKv2

Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning
Select a sampling method

Define the search space

Define the primary metric

Define early termination options

Prepare a model for deployment (20–25%)
Run model training scripts
Configure job run settings for a script

Configure compute for a job run

Consume data from a data asset in a job

Run a script as a job by using Azure Machine Learning

Use MLflow to log metrics from a job run

Use logs to troubleshoot job run errors

Configure an environment for a job run

Define parameters for a job

Implement training pipelines
Create a pipeline

Pass data between steps in a pipeline

Run and schedule a pipeline

Monitor pipeline runs

Create custom components

Use component-based pipelines

Manage models in Azure Machine Learning
Describe MLflow model output

Identify an appropriate framework to package a model

Assess a model by using responsible AI guidelines

Deploy and retrain a model (10–15%)
Deploy a model
Configure settings for online deployment

Configure compute for a batch deployment

Deploy a model to an online endpoint

Deploy a model to a batch endpoint

Test an online deployed service

Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices
Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

Automate model retraining based on new data additions or data changes

Define event-based retraining triggers

Before attending this course, students must have: A fundamental knowledge of Microsoft Azure Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib. Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Design and prepare a machine learning solution (20–25%) Explore data and train models (35–40%) Prepare a model for deployment (20–25%) Deploy and retrain a model (10–15%)

Following your booking, a confirmation message will be sent to all participants, ensuring you're well-informed of your successful enrollment. Calendar placeholders will also be dispatched to assist you in scheduling your commitments around the course. Rest assured, all course materials and access to necessary labs or platforms will be provided no later than one week before the course begins, allowing you ample time to prepare and engage fully with the learning experience ahead.

Our comprehensive training package includes all the necessary materials and resources to facilitate a full learning experience. Enrollees will be provided with detailed course content, encompassing a wide array of topics to ensure a thorough understanding of the subject matter. Additionally, participants will receive a certificate of completion to recognize their dedication and hard work. It's important to note that while the course fee covers all training materials and experiences, the examination fee for certification is not included but can be purchased separately.

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