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AIGP > Module 3 > Flashcards

Module 3 Flashcards

1
Q

List the 4 phases of the AI development lifecycle

A
  1. Planning
  2. Design
  3. Development
  4. Implementation
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2
Q

What needs to be done in the planning stage of the AI development lifecycle?

A
  • Determine the business objectives and requirements
  • Determine the scope of the project
  • Determine the governance structure and responsibilities
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3
Q

List the main types of business problems

A
  • Classification business problem
  • Regression business problem
  • Recommendation business problem
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4
Q

What is a classification business problem?

A

You want to use an AI system to classify data into different types

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5
Q

What is a regression business problem?

A

You are looking for an AI system to predict what you should do in the future based on past data

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6
Q

What is a recommendation business problem?

A

AI is used for recommendations of what to do for a particular problem, such as viewer recommendations or product recommendations

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7
Q

List key questions to help define the business problem

A
  • What type of results are you expecting from the use of the AI system, what do you want to happen, how would you like to solve the problem
  • What processes are currently in use to solve the problem that you can leverage as part of your AI system
  • What type of improvement in what you are doing are you expecting
  • What are the key performance indicators you want to track to understand how well the AI system is performing later
  • What resources do you have available to use to solve the business problem
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8
Q

How do you figure out the answers to your business problems?

A
  • Conduct user interviews
  • Conduct market research on AI systems
  • Identify AI use cases
  • Use the right data
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9
Q

Why is it important to conduct market research?

A

To understand what types of AI systems are available, how they are used and what type of AI system fits into your organization for the problem you are looking to solve

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10
Q

What should you consider when identifying AI use cases?

A

Focus on the organizational mission, what is your mission, what do you do, what’s important to you, what are your main goals, where are the gaps in reaching those goals

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11
Q

During the planning stage, how do you identify the right data?

A

See what is accessible to you, look at existing areas where you obtain data but also new areas that may offer new data

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12
Q

What should you consider when scoping a project?

A
  • Use cases – what your business needs are, then prioritize which problem you want to solve first
  • Impact of AI system for the problem – will the impact be big, will it solve the problem, what effort and resources are required, how long is it going to take
  • Fit of AI system – how well does the use of an AI system fit with the goals of your organization and the problems you want to solve
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13
Q

What are the first things you should do to set up a governance structure?

A
  • Look at existing governance structures
  • Identify an executive to be a champion
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14
Q

What key things should you clarify in your governance structure?

A
  • Who is responsible for maintaining and implementing the AI governance structure
  • Who writes AI policies and procedures
  • Who is responsible for monitoring development and testing, and selecting particular AI systems
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15
Q

What should be included in your data strategy during the design stage of the AI development lifecycle?

A
  • Data gathering
  • Wrangling
  • Cleansing
  • Labelling
  • Applying PETs
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16
Q

What questions should you ask yourself during data gathering?

A
  • What data is required
  • How is the data collected
  • Where is the data stored
  • Do you want pre-trained data
  • Do you want to use internal or external data
  • Does the quality of data fit your needs
  • What is the format of the data (structured/unstructured, static/streaming)
17
Q

What is data wrangling?

A

Preparing the data

18
Q

Describe the data wrangling stage

A
  • About 80% of the lifecycle
  • Taking raw data and converting it to valuable information
  • Cleansing
  • Labelling
  • Applying PETs
19
Q

List the 5 Vs used in data wrangling

A
  • Volume
  • Velocity – how often is it updated or changed
  • Variety – structured, unstructured, etc.
  • Veracity – how accurate and trustworthy is it, is the data from a trusted source
  • Value – what is the outcome of the AI system and do you have the right data to do that
20
Q

List some PETs

A
  • Anonymization – removing identifiers from data
  • Minimization – if data is not needed in the application, do not use it to train the model
  • Differential privacy
  • Federated learning
21
Q

What is Differential privacy?

A

Blurs data by applying an algorithm which enables you to modify the data to keep it meaningful but non-specific – you are not able to identify individuals, but you can still use the data

22
Q

Provide an example of differential privacy

A

Altering the age of individuals in the dataset by a random number between 1 and 5

23
Q

What is federated learning?

A

A new way to train models where you don’t need to share data that might be sensitive among different locations
- You have one central model in a central location (for example in the cloud)
- Each different local location downloads the central model and trains it on the data within their location
- The results of the training are sent back up to the central location to be aggregated

24
Q

What should you consider when choosing the AI system architecture and select a model?

A
  • Choose the algorithm according to the desired level of accuracy and interpretability of the data
  • Think about what you want to learn from your data and how it is going to help solve your problem
  • What are the other requirements or constraints (for example, time constraints)
25
Q

List the steps in developing and implementing an AI model

A
  • Build the model
  • Perform feature engineering
  • Perform model training
  • Perform model testing and validation
  • Perform readiness assessments
  • Deploy the model into production
  • Monitor and validate the model
  • Maintain the model
26
Q

Describe the Perform feature engineering step when developing an AI model

A
  • Define the features of the model
  • Use the same features for training and testing the model so you don’t have inconsistencies
  • Work with subject matter experts to select the features
  • Avoid unnecessary features that you don’t need which make it more difficult to ensure you are testing properly
27
Q

What is feature engineering?

A

You create features from raw data so your predictive models can deeply understand the dataset and then they will perform well on the unseen data – you do that by using domain knowledge from subject matter experts

28
Q

How do you perform model training?

A
  • Train, test, evaluate, and retrain models to determine the best model to use and the best settings are to achieve the desired outcome for your AI system
  • This is very iterative fine tuning
29
Q

How do you perform model testing evaluation?

A
  • Test your model on relevant evaluation metrics
  • These metrics should have been defined earlier, as soon as you knew the system requirements
  • Test on new data, not just your training data, to ensure that the models generalize well and meet your business goals overall
30
Q

What questions do you need to answer when performing readiness assessment?

A
  • Is it ready to deploy
  • Does it do what you want it to do
  • Did your tests come out well
  • Are there any data quality issues
31
Q

What is included in monitoring and validating the model?

A
  • Before deploying, you need to think about continuously monitor the AI model
  • Define a baseline to measure future iterations
  • Maintain the model
32
Q

Why is it important to maintain an AI model?

A

AI systems require extra attention, more so than many other types of systems, because of the complexity of the environment and potential changes in data

AIGP (9 decks)

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