This class was created by Brainscape user Manuel Tupas. Visit their profile to learn more about the creator.
By:
Manuel Tupas
- decks
- flashcards
- learners
Decks in this class (56)
Path1.Mod1.a - Explore ML Workspace - Setting Up Your Workspace
Fives built in rbacs for controll...,
Sequence for creating an ml service,
Sequence for creating an ml service
7
cards
Path1.Mod1.b - Explore ML Workspace - Team Workspace Setups
Pros and cons for workspace per t...,
Pros and cons for workspace per t...,
Pros and cons for workspace per t...
9
cards
Path1.Mod1.c - Explore ML Workspace - Environment Setups
Pros and cons for workspace per t...,
Pros and cons for workspace per t...,
Pros and cons for workspace per t...
9
cards
Path1.Mod1.d - Explore ML Workspace - Regional Setups
Pros and cons for workspace per t...,
Pros and cons for workspace per t...,
Pros and cons for workspace per t...
9
cards
Path1.Mod1.e - Explore ML Workspace - Azure ML Resources and Assets
The three ml resources,
Ci cc ic acthe four compute resou...,
The two auto created datastores o...
8
cards
Path1.Mod1.f - Explore ML Workspace - MLModel Format
Difference between artifacts and ...,
Fl sithe mlmodel format used in m...,
Model flavors
12
cards
Path1.Mod1.g - Explore ML Workspace - Train Models in the Workspace
You know this from ai 900four opt...,
Ideal usage scenario for azure ml...,
Ideal usage scenario for automate...
9
cards
Path1.Mod1.h - Explore ML Workspace - Model Metrics and Evaluation
Regression,
Regression,
Regression
9
cards
Path1.Mod2.a - Explore Workspace Developer Tools - ML Studio
The three tools for azure ml,
The three tools for azure ml,
The three tools for azure ml
10
cards
Path1.Mod2.a - Explore Workspace Developer Tools - Azure ML with CLI
Advantages of using azure cli in ml,
What azure cli installation is ba...,
What azure cli installation is ba...
10
cards
Path1.Mod2.c - Explore Workspace Developer Tools - Python SDK
The command for installing the py...,
Python sdk the min version requir...,
Requirements for connecting to a ...
7
cards
Path2.Mod1.a - Make Data Available
Datastores,
Datastores,
B fs dlg1 dlg2four datastore types
13
cards
Path2.Mod1.b - Make Data Available - Creating Datastores
3 cli 3 pythonsix ways to create ...,
3 cli 3 pythonsix ways to create ...,
3 cli 3 pythonsix ways to create ...
11
cards
Path2.Mod1.c - Make Data Available - Creating Data Assets
3 cli 3 pythonsix ways to create ...,
3 cli 3 pythonsix ways to create ...,
Context for using an mltable data...
11
cards
Path3.Mod1.a - Automated Machine Learning - What is it?
General advantages of automl,
Automatedml advantages,
Six steps for designing and runni...
7
cards
Path3.Mod1.b - Automated Machine Learning - Featurization and Models
Differences between training data...,
Differences between training data...,
Feature engineering featurization...
9
cards
Path3.Mod1.c - Automated Machine Learning - Overfitting
How overfitting occurs,
Consider the following data model...,
Best practices for preventing ove...
7
cards
Path3.Mod1.d - Automated Machine Learning - Prep & Run an AutoML Experiment
Scaling and normalization must be...,
Scaling and normalization must be...,
Scaling and normalization must be...
11
cards
Path3.Mod1.e - Automated Machine Learning - Prep & Run AutoML Experiment Code
The relationship between a data a...,
The relationship between a data a...,
Explain what this code is doing f...
9
cards
Path3.Mod1.f - Automated Machine Learning - Evaluate and Compare Models In ML Studio
Ml studio automl experiment overv...,
Cb mfv hcfthree data guardrails a...,
Cb mfv hcfthree data guardrails a...
6
cards
Path3.Mod1.g - Automated Machine Learning - Metric Effects and Meanings
The primary differences between m...,
The primary differences between m...,
Selecting an evaluation metric fo...
9
cards
Path3.Mod1.h - Automated Machine Learning - Chart Analysis
Good vs bad confusion matrix,
Good vs bad roc curve,
Good vs bad precision recall curve
10
cards
Path4.Mod1.a - Training Models with Scripts - Run a Training Script as a Command Job
Three things to create a producti...,
C c e c d_n e_nparameters to conf...,
How to use parameters in your scr...
9
cards
Path4.Mod1.b - Training Models with Scripts - Specifying an Environment for a Command Job
We de cespecifying an environment...,
We de cespecifying an environment...,
We de cespecifying an environment...
7
cards
Path4.Mod2.a - Training Models with Scripts - Track Model Training with Jobs using MLFlow
Two options to track ml jobs with...,
Two options to track ml jobs with...,
Two options to track ml jobs with...
6
cards
Path4.Mod2.b - Training Models with Scripts - Code to support Model Tracking with Jobs using MLFlow
L_e s_rretrieve metrics with mlfl...,
L_e s_rretrieve metrics with mlfl...,
L_e s_rretrieve metrics with mlfl...
9
cards
Path4.Mod2.c - Training Models with Scripts - Code to support Experiment Tracking with Jobs using MLFlow
Benefits of tracking experiments,
Mlflow for tracking,
Prereqs for using mlflow
7
cards
Path4.Mod3.a - Perform Hyperparameter Tuning
The diff between parameters and h...,
Hyperparameter tuning general app...,
Asdf
10
cards
Path4.Mod3.b - Perform Hyperparameter Tuning - Sweep Job Sampling Methods
Pros cons of grid sampling,
Pros cons of grid sampling,
Pros cons of grid sampling
10
cards
Path4.Mod3.c - Perform Hyperparameter Tuning - Sweep Job Early Termination
E_i d_e bp msp tsptwo parameters ...,
E_i d_e bp msp tsptwo parameters ...,
Bandit policy
8
cards
Path4.Mod3.d - Perform Hyperparameter Tuning - Sweep Job Implementation
Two things required for creating ...,
You must create an instance of th...,
Convert to a sweep job
9
cards
Path5.Mod1.a - Run Pipelines - Creating a Component
Creating and using components,
M i ccethree parts to a component,
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...,
D1 d2 sb rd1 rd2the execute pytho...,
D1 d2 sb rd1 rd2the execute pytho...
8
cards
Path5.Mod1.c - Run Pipelines - Creating and Running a Pipeline Job
Pipelines run as while each compo...,
Library sdk 1 where pipeline live...,
Pipeline yaml files are created i...
7
cards
Path5.Mod1.d - Run Pipelines - Schedules and Triggers
Js rt fr in use these two things ...,
Js rt fr in use these two things ...,
Js rt fr in use these two things ...
6
cards
Path6.Mod1.a - Deploy and Consume Models - Managed Online Endpoints
Real time endpoints inferencing,
Moe koetwo types of online endpoi...,
Ma ss env cconffour things requir...
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 for b...,
When to use batch endpoints for b...,
Invoking a batch endpoint does th...
10
cards
Path6.Mod2.a - Deploy and Consume Models - Batch Endpoint Deployment
Wrt batch endpoint deployments us...,
This is required for an mlflow mo...,
This is required for an mlflow mo...
6
cards
Path6.Mod2.b - Deploy and Consume Models - Batch Endpoint Deployment w/out MLFlow
When deploying without mlflow all...,
Two functions the scoring script ...,
Given this code note azureml_mode...
9
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,
Three ways to create a rai dashboard
9
cards
Path7.Mod1.c - Responsible AI Dashboard - Evaluate the RAI Dashboard
Depending on the components selec...,
Describe error analysis two graph...,
Etmtwo visual representations for...
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
More about
dp-100
- Class purpose General learning
Learn faster with Brainscape on your web, iPhone, or Android device. Study Manuel Tupas's DP-100 flashcards for their Univ. of CA class now!
How studying works.
Brainscape's adaptive web mobile flashcards system will drill you on your weaknesses, using a pattern guaranteed to help you learn more in less time.
Add your own flashcards.
Either request "Edit" access from the author, or make a copy of the class to edit as your own. And you can always create a totally new class of your own too!
What's Brainscape anyway?
Brainscape is a digital flashcards platform where you can find, create, share, and study any subject on the planet.
We use an adaptive study algorithm that is proven to help you learn faster and remember longer....
Looking for something else?
DP-100: MS-Learn AML Labs
- 1 decks
- 64 flashcards
- 5 learners
Decks:
01 Getting Started With Azure Ml, And more!
