Model life cycle Flashcards
AI Model Life Cycle
The model life cycle refers to the cyclical process that AI and machine learning projects follow, encompassing stages from project scoping, design/building the model, to deployment in production.
Scoping
First, this stage involves the planning and motivational aspects of your project.
It is important to start strong if you want your artificial intelligence project to be successful. There’s a great phrase that characterizes this project stage: garbage in, garbage out.
This means if the data you collect is no good, you won’t be able to build an effective AI algorithm, and your whole project will collapse.
The Design phase
The Design phase is essentially an iterative process comprising all the steps relevant to building the AI or machine learning model: data acquisition, exploration, preparation, cleaning, feature engineering, testing and running a set of models to try to predict behaviours or discover insights in the data
While the fundamental testing concepts are fully applicable in AI development projects, there are additional considerations too.
The volume of test data can be large, which presents complexities.
Human biases in selecting test data can adversely impact the testing phase, therefore, data validation is important.
Your testing team should test the AI and ML algorithms keeping model validation, successful learnability, and algorithm effectiveness in mind.
Regulatory compliance testing and security testing are important since the system might deal with sensitive data, moreover, the large volume of data makes performance testing crucial.
You are implementing an AI solution that will need to use data from your other systems, therefore, systems integration testing assumes importance.
Test data should include all relevant subsets of training data, i.e., the data you will use for training the AI system.
Your team must create test suites that help you validate your ML models.
What is the significance of project scoping in the model life cycle?
In this phase, it’s crucial to precisely define the strategic business objectives and desired outcomes of the project, select align all the different stakeholders’ expectations, anticipate the key resources and steps, and define the success metrics. Selecting the AI or machine learning use cases and being able to evaluate the return on investment (ROI) is critical to the success of any data project.
Why is model validation important during the design/building phase?
Model validation assesses the performance of the model iterations, ensuring they align with the defined ROI objective and help in selecting the best model.
How does visual modeling contribute to AI development?
Visual modeling enhances productivity by assisting in feature engineering, algorithm selection, and hyperparameter optimization.
What role do development tools like DataRobot, H2O, and Watson Studio play in AI development?
Development tools offer platforms for streamlined data exploration, model building, deployment, and collaboration among team members.
Why is testing important in the model life cycle?
Testing ensures the model’s accuracy, effectiveness, regulatory compliance, security, and performance in handling real-world scenarios.
How does the volume of test data affect testing in AI development?
Large volumes of test data can lead to complexities in testing due to increased processing requirements and potential biases.
Why is human bias in selecting test data a concern during testing?
Human bias can lead to skewed testing outcomes, impacting the model’s performance and generalization.
Why is systems integration testing important in AI projects?
Systems integration testing ensures that the AI solution seamlessly interacts with other systems, promoting smooth data flow and functionality.
What is the importance of test data inclusion during testing?
Including relevant subsets of training data in the test suite ensures comprehensive testing, addressing various scenarios.
