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Design and prepare a machine learning solution

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

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

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 (NLP)
– 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 SDK
– 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

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

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

Learn Designing and Implementing Data Science Solution on Azure – DP100 Course in Pune with Training, Certification & Guaranteed Job Placement Assistance!


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