考试代码：307 Artificial Intelligence 考试大纲

ITS认证考试考什么？考试内容？

ITS认证考试是分科目的，现在我们要看得就是Certiport给出的ITS考试代码：307 考试名称：Artificial Intelligence（人工智能）的大纲，如果你通过了这科考试之后，将会获得Artificial Intelligence的ITS证书

本ITS考试的考生，通过了解如何使用人工智能解决问题，为人工智能的专业使用做好相关准备。

Defining the AI problem

Identify the problem we are trying to solve using AI (e.g., user segmentation, improving customer service)
- Identify the need that will be addressed
- Find out what information comes in and what output is expected
- Determine whether AI is called for
- Consider upsides and downsides of AI in the situation
- Define measurable success
- Benchmark against domain or organization-specific risks to which the project may be susceptible
Classify the problem (e.g., regression, unsupervised learning)
- Examine available data (labeled or unlabeled?) and the problem
- Determine problem type (e.g., classifier, regression, unsupervised, reinforcement)
Identify the areas of expertise needed to solve the problem
- Identify business expertise required
- Identify need for domain (subject-matter) expertise on the problem
- Identify AI expertise needed
- Identify implementation expertise needed
Build a security plan
- Consider internal access levels or permissions
- Consider infrastructure security
- Assess the risk of using a certain model or potential attack surfaces (e.g., adversarial attacks on real-time learning model)
Ensure that AI is used appropriately
- Identify potential ways that the AI can mispredict or harm specific user groups
- Set guidelines for data gathering and use
- Set guidelines for algorithm selection from user perspective
- Consider how the subject of the data can interpret the results
- Consider out-of-context use of AI results
Choose transparency and validation activities
- Communicate intended purpose of data collection
- Decide who should see the results
- Review legal requirements specific to the industry with the problem being solved

Managing data to solve the AI problem

Choose the way to collect data
- Determine type/characteristics of data needed
- Decide if there is an existing data set or if you need to generate your own
- When generating your own dataset, decide whether collection can be automated or requires user input
Assess data quality
- Determine if dataset meets needs of task
- Look for missing or corrupt data elements
Ensure that data are representative
- Examine collection techniques for potential sources of bias
- Make sure the amount of data is enough to build an unbiased model
Identify resource requirements (e.g., computing, time complexity)
- Assess whether problem is solvable with available computing resources
- Consider the budget of the project and resources that are available
Convert data into suitable formats (e.g., numerical, image, time series)
- Convert data to binary (e.g., images become pixels)
- Convert computer data into features suitable for AI (e.g., sentences become tokens)
Select features for the AI model
- Determine which features of data to include
- Build initial feature vectors for test/train dataset
- Consult with subject-matter experts to confirm feature selection
Engage in feature engineering
- Review features and determine what standard transformations are needed
- Create processed datasets
Identify training and test data sets
- Separate available data into training and test sets
- Ensure test set is representative
Document data decisions
- List assumptions, predicates, and constraints upon which design choices have been reasoned
- Make this information available to regulators and end users who demand deep transparency

Building an AI model that solves the problem

Consider applicability of specific algorithms
- Evaluate AI algorithm families
- Decide which algorithms are suitable, e.g., neural network, classification (like decision tree, k means)
Train a model using the selected algorithm
- Train model for an algorithm with best-guess starting parameters.
- Tune the model by changing parameters
- Gather performance metrics for the model
- Iterate as needed
Select specific model after experimentation, avoiding overengineering
- Consider cost, speed, and other factors in evaluating models
- Determine whether selected model meets explainability requirements
Tell data stories
- Where feasible, create visualizations of the results
- Look for trends
- Verify that the visualization is useful for making a decision
Evaluate model performance (e.g., accuracy, precision)
- Check for overfitting, underfitting
- Generate metrics or KPIs
- Introduce new test data to cross-validate robustness, testing how model handles unforeseen data
Look for potential sources of bias in the algorithm
- Verify that inputs resemble training data
- Confirm that training data do not contain irrelevant correlations we do not want classifier to rely on
- Check for imbalances in data
- Guard against creating self-fulfilling prophecies based upon historical biases
- Check the explainability of the algorithm (e.g., feature importance in decision trees)
Evaluate model sensitivity
- Test for sensitivity of model
- Test for specificity of model
Confirm adherence to regulatory requirements, if any
- Evaluate outputs according to thresholds defined in requirements
- Document results
Obtain stakeholder approval
- Collect results and benchmark risks
- Hold sessions to evaluate solution

Deploying model in an application

Train customers on how to use product and what to expect from it
- Inform users of model limitations
- Inform users of intended model usage
- Share documentation
- Manage customer expectations
Plan to address potential challenges of models in production
- Understand the types of challenges you are likely to encounter
- Understand the indicators of challenges
- Understand how each type of challenge could be mitigated
Design a production pipeline, including application integration
- Create a pipeline (training, prediction) that can meet the product needs (may be different from the experiment)
- Find the solution that works with the existing data stores and connects to the application
- Build the connection between the AI and the application
- Build mechanism to gather user feedback
- Test accuracy of AI through application
- Test robustness of AI
- Test speed of AI
- Test application to fit size of use case (e.g., in AI for mobile applications)
Support the AI solution
- Document the functions within the AI solution to allow for maintenance (updates, fixing bugs, handling edge cases)
- Train a support team
- Implement a feedback mechanism
- Implement drift detector
- Implement ways to gather new data

Monitoring live production

Engage in oversight
- Log application and model performance to facilitate security, debug, accountability, and audit
- Use robust monitoring systems
- Act upon alerts
- Observe the system over time in a variety of contexts to check for drift or degraded modes of operation
- Detect any way system fails to support new information
Assess business impact (key performance indicators)
- Track impact metrics to determine whether solution has solved the problem
- Compare previous metrics with new metrics when changes are made
- Act on unexpected metrics by finding problem and fixing it
Measure impacts on individuals and communities
- Analyze impact on specific subgroups
- Identify and mitigate issues
- Identify opportunities for optimization
Handle feedback from users
- Measure user satisfaction
- Assess whether users are confused (e.g., do they understand what the AI is supposed to do for them?)
- Incorporate feedback into future versions
Consider improvement or decommission on a regular basis
- Combine impact observations (e.g., business, community, technology trends) to assess AI value
- Decide whether to retrain AI, continue to use AI as is, or to decommission AI