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Manuel Tupas
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Decks in this class (58)
Path1.Mod1.a - Explore ML Workspace - Setting Up Your Workspace
Six built in rbacs for controllin...,
Sequence for creating an ml servi...,
The four azure resources created ...
7
cards
Path1.Mod1.b - Explore ML Workspace - Team Workspace Setups
Workspace per team setup use case 1,
Workspace per team setup pros 2,
Workspace per team setup cons 3
9
cards
Path1.Mod1.c - Explore ML Workspace - Environment Setups
Single environment workspace use ...,
Single environment workspace pros 2,
Single environment workspace cons 3
9
cards
Path1.Mod1.d - Explore ML Workspace - Regional Setups
Regional training use case 1,
Regional training pros 2,
Regional training cons 3
9
cards
Path1.Mod1.e - Explore ML Workspace - Azure ML Resources and Assets
The three ml resources 1,
The four compute resources 2,
The two auto created datastores f...
8
cards
Path1.Mod1.f - Explore ML Workspace - MLModel Format
Difference between artifacts and ...,
Mflow s mlmodel format what it is...,
Model flavors what they are how t...
12
cards
Path1.Mod1.g - Explore ML Workspace - Train Models in the Workspace
Four options for training models ...,
When to use azure ml designer 2,
When to use automated ml 3
9
cards
Path1.Mod1.h - Explore ML Workspace - Model Metrics and Evaluation
The five metrics for evaluating r...,
The confusion matrix found in eva...,
The four metrics derived from the...
9
cards
Path1.Mod2.a - Explore Workspace Developer Tools - ML Studio
The three tools for azure ml and ...,
Ideal use cases for ml studio 2,
Ideal use cases for azure ml pyth...
10
cards
Path1.Mod2.a - Explore Workspace Developer Tools - Azure ML with CLI
Advantages of using azure cli in ...,
Azure cli installation is based o...,
You don t need to install azure c...
10
cards
Path1.Mod2.c - Explore Workspace Developer Tools - Python SDK
The command for installing the py...,
The min python version required t...,
Mlclient class usage and four req...
7
cards
Path2.Mod1.a - Make Data Available
Datastores what they are 1,
Datastores their advantages and w...,
Four datastore types 3
13
cards
Path2.Mod1.b - Make Data Available - Creating Datastores
Create an azure blob datastore th...,
Create an azure blob datastore th...,
Create an azure data lake gen 2 d...
11
cards
Path2.Mod1.c - Make Data Available - Creating Data Assets
Creating a data asset uri file su...,
Behavior when creating a local da...,
The context for using an mltable ...
11
cards
Path3.Mod1.a - Automated Machine Learning - What is it?
Automatedml defined 1,
Automatedml advantages 2,
Six steps for designing and runni...
7
cards
Path3.Mod1.b - Automated Machine Learning - Featurization and Models
Differences between training data...,
The problem with validation data ...,
Define feature engineering featur...
9
cards
Path3.Mod1.c - Automated Machine Learning - Overfitting
How overfitting occurs 1,
Consider the following data 2,
Best practices the user implement...
7
cards
Path3.Mod1.d - Automated Machine Learning - Prep & Run an AutoML Experiment
During automl experimentation sca...,
Once experimentation completes on...,
Automl performs featurization by ...
11
cards
Path3.Mod1.e - Automated Machine Learning - Prep & Run AutoML Experiment Code
Order of operations between a dat...,
How to specify the data set as in...,
Explain what this code is doing 3
9
cards
Path3.Mod1.f - Automated Machine Learning - Evaluate and Compare Models In ML Studio
Ml studio automl experiment overv...,
Data guardrails where they are lo...,
Data guardrails are applied when 3
6
cards
Path3.Mod1.g - Automated Machine Learning - Metric Effects and Meanings
Describe what macro micro and wei...,
Describe how class imbalance affe...,
Selecting an evaluation metric fo...
9
cards
Path3.Mod1.h - Automated Machine Learning - Chart Analysis
Good vs bad confusion matrix 1,
Good vs bad roc curve 2,
Good vs bad precision recall curv...
10
cards
Path4.Mod1.a - Training Models with Scripts - Run a Training Script as a Command Job
Three actions for creating a prod...,
Parameters to configure a command...,
Using parameters in your script w...
9
cards
Path4.Mod1.b - Training Models with Scripts - Specifying an Environment for a Command Job
When specifying an environment to...,
Describe the workspace environment 2,
Code to get all workspace environ...
7
cards
Path4.Mod2.a - Training Models with Scripts - Track Model Training with Jobs using MLFlow
Two options to track ml jobs with...,
Two libraries you ll need to pip ...,
When and where to enable mlflow a...
6
cards
Path4.Mod2.b - Training Models with Scripts - Code to support Model Tracking with Jobs using MLFlow
Code that lists all experiments w...,
Code that retrieves runs within a...,
Code that searches for all experi...
9
cards
Path4.Mod2.c - Training Models with Scripts - Code to support Experiment Tracking with Jobs using MLFlow
Benefits of tracking experiments 1,
Main benefit when using mlflow fo...,
General prerequisites for using m...
7
cards
Path4.Mod3.a - Perform Hyperparameter Tuning (Continuous vs Discontinuous)
The diff between parameters and h...,
Define hyperparameter tuning 2,
Four general steps in a sweep job...
10
cards
Path4.Mod3.b - Perform Hyperparameter Tuning - Sweep Job Sampling Methods (Sampling)
Grid sampling defined 1,
Explain grid sampling code 2,
Grid sampling pros and cons 3
10
cards
Path4.Mod3.c - Perform Hyperparameter Tuning - Sweep Job Early Termination
Two common parameters for configu...,
Three policies for configuring an...,
Bandit policy explain slack facto...
8
cards
Path4.Mod3.d - Perform Hyperparameter Tuning - Sweep Job Implementation
Two things required by your train...,
To create a sweep job instantiate...,
Override a command instance s inp...
9
cards
Path5.Mod1.a - Run Pipelines - Creating a Component
Components what they are three re...,
Three parts to a component 2,
Two files required to create a co...
8
cards
Path5.Mod1.b - Run Pipelines - Creating an Execute Python Script Component
Steps to implement the execute py...,
The execute python script compone...,
The execute python script dataset...
8
cards
Path5.Mod1.c - Run Pipelines - Creating and Running a Pipeline Job
Pipelines run as while each compo...,
Sdk 1 module where pipeline lives 2,
Pipeline yaml files are created i...
7
cards
Path5.Mod1.d - Run Pipelines - Schedules and Triggers
Two trigger classes used for sche...,
Explain crontab syntax 2,
Class type to schedule a pipeline...
6
cards
Path6.Mod1.a - Deploy and Consume Models - Managed Online Endpoints
Real time endpoints inferencing 1,
Two types of online endpoints whe...,
Four things required for model de...
9
cards
Path6.Mod1.b - Deploy and Consume Models - Managed Online Endpoint w/out MLFlow
Deploying to an online endpoint w...,
Code for creating an environment ...,
The managedonlinedeployment class...
10
cards
Path6.Mod2.a - Deploy and Consume Models - Batch Endpoints
When to use batch endpoints 1,
Batch inferencing 2,
Batch endpoints when invoked crea...
10
cards
Path6.Mod2.a - Deploy and Consume Models - Batch Endpoint Deployment
Wrt batch endpoint deployments us...,
This specific action must happen ...,
This specific file must be includ...
6
cards
Path6.Mod2.b - Deploy and Consume Models - Batch Endpoint Deployment w/out MLFlow
When deploying without mlflow all...,
The three responsibilities of the...,
Two functions the scoring script ...
10
cards
Path6.Mod2.c - Deploy and Consume Models - Invoke and Troubleshoot Batch Endpoints, Debug Pipelines
Added learning:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2
7
cards
Path7.Mod1.a - Responsible AI Dashboard - General Requirements and Goals
Added due to updates to DP-100 on Oct 18th 2023
https://learn.microsoft.com/en-us/training/modules/manage-compare-models-azure-machine-learning/
12
cards
Path7.Mod1.b - Responsible AI Dashboard - Creating your RAI Dashboard
Four steps to create a responsibl...,
Available tool components 2,
Three tools for creating an rai d...
9
cards
Path7.Mod1.c - Responsible AI Dashboard - Evaluate the RAI Dashboard
Depending on the components selec...,
Describe error analysis 2,
Two visual representations for er...
8
cards
Path7.Mod1.d - Responsible AI Dashboard - Model Performance and Fairness
Augmented learning from:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
7
cards
Path7.Mod1.e - Responsible AI Dashboard - UnFairness Mitigation Algorithms
Augmented learning from:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
https://blogs.microsoft.com/newengland/2021/08/10/maidap-blog-differential-privacy/
7
cards
Path7.Mod1.f - Responsible AI Dashboard - Privacy and Security, Differential Privacy
Augmented Learning:
Privacy and Security
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2
Differential privacy
https://github.com/opendp/smartnoise-core
Counterfit
https://github.com/Azure/counterfit/#Getting-Started
8
cards
Path8.Mod1.a - Intro to DevOps Principles for ML
Additional module on MLOps:
https://learn.microsoft.com/en-us/training/paths/introduction-machine-learn-operations/
8
cards
Path8.Mod1.b - Intro to DevOps Principles for ML - Trigger with Azure ML Events
Augmented learning:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
10
cards
Path8.Mod1.c - Intro to DevOps Principles for ML - Compute Targets
Augmented learning:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target?view=azureml-api-2
9
cards
Path8.Mod1.d - Intro to DevOps Principles for ML - VM Series
Augmented learning:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target?view=azureml-api-2
11
cards
Path9.Mod1.a - Selecting Regression Algorithms for Azure ML
Augmented learning
https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=docs-article-lazzeri&view=azureml-api-1
https://learn.microsoft.com/en-us/azure/machine-learning/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet.png?view=azureml-api-1#lightbox
8
cards
Path9.Mod1.c - Selecting Binary Classification Algorithms for Azure ML
Augmented learning
https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=docs-article-lazzeri&view=azureml-api-1
https://learn.microsoft.com/en-us/azure/machine-learning/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet.png?view=azureml-api-1#lightbox
6
cards
Path9.Mod1.b - Selecting Multi-Classification Algorithms for Azure ML
Augmented learning
https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=docs-article-lazzeri&view=azureml-api-1
https://learn.microsoft.com/en-us/azure/machine-learning/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet.png?view=azureml-api-1#lightbox
6
cards
Path9.Mod1.d - Selecting Text Analyics and Recommender Algorithms for Azure ML
Augmented learning
https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=docs-article-lazzeri&view=azureml-api-1
https://learn.microsoft.com/en-us/azure/machine-learning/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet.png?view=azureml-api-1#lightbox
7
cards
Path9.Mod1.e - Selecting Clustering, Anomaly Detection and Image Classification Algorithms for Azure ML
Augmented learning
https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=docs-article-lazzeri&view=azureml-api-1
https://learn.microsoft.com/en-us/azure/machine-learning/media/algorithm-cheat-sheet/machine-learning-algorithm-cheat-sheet.png?view=azureml-api-1#lightbox
4
cards
Renewal1 - Design an ML Training Solution
Use this service when one of the ...,
Use either of these services if y...,
Use either of these services if y...
12
cards
Renewal2 - Work with Compute Targets
The 5 computer types targets you ...,
Use these compute targets during ...,
Use these compute targets when mo...
9
cards
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DP-100
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