Microsoft Certified: Azure Data Scientist Associate

Create an Azure Machine Learning workspace
create an Azure Machine Learning workspace
configure workspace settings
manage a workspace by using Azure Machine Learning studio

Manage data in an Azure Machine Learning workspace
select Azure storage resources
register and maintain datastores
create and manage datasets

Manage compute for experiments in Azure Machine Learning
determine the appropriate compute specifications for a training workload
create compute targets for experiments and training
configure Attached Compute resources including Azure Databricks
monitor compute utilization

Implement security and access control in Azure Machine Learning
determine access requirements and map requirements to built-in roles
create custom roles
manage role membership
manage credentials by using Azure Key Vault

Set up an Azure Machine Learning development environment
create compute instances
share compute instances
access Azure Machine Learning workspaces from other development environments

Set up an Azure Databricks workspace
create an Azure Databricks workspace
create an Azure Databricks cluster
create and run notebooks in Azure Databricks
link and Azure Databricks workspace to an Azure Machine Learning workspace

Create models by using the Azure Machine Learning designer
create a training pipeline by using Azure Machine Learning designer
ingest data in a designer pipeline
use designer modules to define a pipeline data flow
use custom code modules in designer

Run model training scripts
create and run an experiment by using the Azure Machine Learning SDK
configure run settings for a script
consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
run a training script on Azure Databricks compute
run code to train a model in an Azure Databricks notebook

Generate metrics from an experiment run
log metrics from an experiment run
retrieve and view experiment outputs
use logs to troubleshoot experiment run errors
use MLflow to track experiments
track experiments running in Azure Databricks

Use Automated Machine Learning to create optimal models
use the Automated ML interface in Azure Machine Learning studio
use Automated ML from the Azure Machine Learning SDK
select pre-processing options
select the algorithms to be searched
define a primary metric
get data for an Automated ML run
retrieve the best model

Tune hyperparameters with Azure Machine Learning
select a sampling method
define the search space
define the primary metric
define early termination options
find the model that has optimal hyperparameter values

Select compute for model deployment
consider security for deployed services
evaluate compute options for deployment
deploy a model as a service
configure deployment settings
deploy a registered model
deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
consume a deployed service
troubleshoot deployment container issues

Manage models in Azure Machine Learning
register a trained model
monitor model usage
monitor data drift

Create an Azure Machine Learning pipeline for batch inferencing
configure a ParallelRunStep
configure compute for a batch inferencing pipeline
publish a batch inferencing pipeline
run a batch inferencing pipeline and obtain outputs
obtain outputs from a ParallelRunStep

Publish an Azure Machine Learning designer pipeline as a web service
create a target compute resource
configure an inference pipeline
consume a deployed endpoint

Implement pipelines by using the Azure Machine Learning SDK
create a pipeline
pass data between steps in a pipeline
run a pipeline
monitor pipeline runs

Apply ML Ops practices
trigger an Azure Machine Learning pipeline from Azure DevOps
automate model retraining based on new data additions or data changes
refactor notebooks into scripts
implement source control for scripts

Use model explainers to interpret models
select a model interpreter
generate feature importance data

Describe fairness considerations for models
evaluate model fairness based on prediction disparity
mitigate model unfairness

Describe privacy considerations for data
describe principles of differential privacy
specify acceptable levels of noise in data and the effects on privacy

This course is intended 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.