Short-answered
Questions:
Answer:
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.
Answer:
The three main stages of the model life cycle are project scoping,
design/building the model, and deployment in production.
Answer:
Project scoping involves defining the problem, objectives, and constraints,
ensuring that the project aligns with business goals and sets clear
expectations.
Answer:
The design/building phase involves data acquisition, exploration, preparation,
feature engineering, testing models, and iterative processes to create an AI or
ML model.
Answer:
Model validation assesses the performance of the model iterations, ensuring
they align with the defined ROI objective and help in selecting the best model.
Answer:
Python, R, and Scala are some popular programming languages used in AI
development.
Answer:
Open frameworks like Scikit-learn, XGBoost, and TensorFlow provide pre-built
tools and libraries for AI tasks, streamlining model development.
Answer:
Visual modeling enhances productivity by assisting in feature engineering,
algorithm selection, and hyperparameter optimization.
Answer:
Development tools offer platforms for streamlined data exploration, model
building, deployment, and collaboration among team members.
Answer:
Testing ensures the model's accuracy, effectiveness, regulatory compliance,
security, and performance in handling real-world scenarios.
Answer:
Large volumes of test data can lead to complexities in testing due to increased
processing requirements and potential biases.
Answer:
Human bias can lead to skewed testing outcomes, impacting the model's
performance and generalization.
Answer:
The large data volume requires careful performance testing to ensure the model
can handle processing efficiently.
Answer:
Systems integration testing ensures that the AI solution seamlessly interacts
with other systems, promoting smooth data flow and functionality.
Answer:
Including relevant subsets of training data in the test suite ensures
comprehensive testing, addressing various scenarios.
Answer:
A narrative provides context, meaning, and relevance to data, making it more
engaging and relatable to the audience.
Answer:
Stories that incorporate data provide evidence-backed insights, making them
more persuasive compared to anecdotes or personal experiences.
Answer: Understanding the audience,
choosing the right data and visualizations, drawing attention to key information,
developing a narrative, and engaging the audience.
Answer:
Data storytelling enhances cross-cultural understanding by sharing experiences,
identities, and insights across different communities.
Answer:
Stories simplify complex information, making it memorable and easier to retain,
aiding in consistent communication of results.
Answer:
Challenges in executing a capstone project may include technical difficulties,
changing requirements, resource limitations, and unforeseen obstacles.
Answer:
Project management involves organizing tasks, allocating resources, and
ensuring efficient collaboration among team members, leading to successful
project completion.
Answer:
A mentor provides guidance, feedback, and expertise, helping students navigate
challenges and enhance the quality of the capstone project.
Answer:
Ethical considerations impact capstone projects by influencing data usage,
privacy, fairness, and potential social implications of the project's outcomes.
Answer:
Documentation provides transparency, accountability, and a reference for
progress, findings, and outcomes, aiding in effective project management.
Answer:
Aligning with learning outcomes ensures that students achieve educational
goals, practical experience, and demonstrate mastery of AI concepts.
Answer:
Collaboration fosters teamwork, diverse perspectives, and shared responsibility
in addressing complex challenges in group-based capstone projects.
Answer:
Reflection allows students to analyze project progress, outcomes, successes,
and areas for improvement, promoting self-awareness and continuous growth.
Answer:
A completed capstone project showcased in a portfolio demonstrates skills,
achievements, problem-solving abilities, and practical experience, enhancing
career prospects.
Answer:
An effective scope defines the project's boundaries, objectives, and
limitations, preventing ambiguity and guiding project execution. Question: What is
the purpose of the model design phase in the model life cycle? Answer:
The model design phase involves planning how to structure the AI or ML model,
selecting algorithms, and defining the approach to solving the problem.
Answer:
Data acquisition involves gathering relevant data for the model's training,
ensuring it's comprehensive and representative of the problem.
Answer:
Feature engineering involves selecting, transforming, or creating features from
the raw data to improve the model's performance and predictive capabilities.
Answer:
Data exploration and preprocessing help uncover patterns, anomalies, and
missing values, ensuring the data is suitable for training the model.
Answer:
Testing different models helps in identifying the most suitable algorithm or
approach that produces the desired results.
Answer:
Hyperparameter optimization fine-tunes the model's parameters, leading to
improved performance and better generalization to new data.
Answer:
Model validation assesses each model's performance against the defined
objectives, aiding in choosing the model with the highest accuracy or desired
outcome.
Answer:
Some popular open-source frameworks for machine learning include Scikit-learn,
TensorFlow, PyTorch, and Keras.
Answer:
The key goal of the model building phase is to create a machine learning model
that effectively addresses the defined problem and produces accurate
predictions.
Answer:
Iterative testing and refining ensure that the model is continuously improved
based on its performance, enhancing its accuracy and effectiveness.
Answer:
The choice of algorithms determines the model's behavior and capabilities,
affecting its ability to learn and predict outcomes.
1. The ___________ is a cyclical process that AI or machine learning projects follow.
Answer: Model Life Cycle
2. The first stage of the model life cycle is ___________ where the project's scope, objectives, and stakeholders' expectations are defined.
Answer: Project Scoping
3. The ___________ phase involves tasks like data acquisition, exploration, preparation, and testing various models.
Answer: Design/Build Phase
4. The process of dividing a dataset into training and testing subsets to assess model performance is known as ___________.
Answer: Train-Test Split
5. The ___________ phase involves deploying the trained model into a real-world environment for practical use.
Answer: Deployment
6. During the model development, ___________ is essential to determine, measure, and evaluate the model's performance.
Answer: Model Validation
7. Open languages such as ___________, ___________, and ___________ are commonly used in AI development.
Answer: Python, R, Scala
8. Scikit-learn, XG Boost, and TensorFlow are examples of ___________ frameworks used in AI development.
Answer: Open frameworks
9. Visual modeling, AutoAI, and hyperparameter optimization are productivity-enhancing capabilities provided by AI ___________.
Answer: Development tools
10. ___________ testing is important when implementing an AI solution that deals with sensitive data.
Answer: Regulatory compliance
11. Systems integration testing is important when integrating AI solutions with other ___________ systems.
Answer: Systems
12. A narrative combined with data and visuals in data storytelling helps ___________ what's happening in the data set.
Answer: explain
13. Closer scrutiny of data relationships can help uncover how each ___________ relates to others.
Answer: data point
14. Data storytelling incorporates ___________ and analytics, making it more convincing than anecdotes alone.
Answer: data
15. The main elements that make a compelling data story include data, visuals, and ___________.
Answer: narrative