Learn about the generative AI model lifecycle, a framework that guides you through the process of creating new content using deep learning algorithms, from ideation to deployment.
Table of Contents
Question
Which of the following stages are part of the generative AI model lifecycle mentioned in the course? (Select all that apply)
A. Selecting a candidate model and potentially pre-training a custom model.
B. Manipulating the model to align with specific project needs.
C. Defining the problem and identifying relevant datasets.
D. Performing regularization
E. Deploying the model into the infrastructure and integrating it with the application.
Answer
A. Selecting a candidate model and potentially pre-training a custom model.
C. Defining the problem and identifying relevant datasets.
E. Deploying the model into the infrastructure and integrating it with the application.
Explanation
The correct answers are A, C, and E. These are the stages that are part of the generative AI model lifecycle mentioned in the course. The course follows the framework proposed by Segmind, which consists of five steps:
- Ideation: This is where you define the problem and identify relevant datasets that can help you achieve your goal. You also research existing models and solutions to see if your idea is feasible and novel.
- Data Collection and Preparation: This is where you select and preprocess the data that you will use to train your model. You also perform data analysis and exploration to understand the characteristics and distribution of your data.
- Model Design and Training: This is where you select a candidate model and potentially pre-train a custom model using your data. You also design and implement the model architecture, such as a GAN or a VAE, and tune the hyperparameters to optimize the model performance.
- Model Evaluation: This is where you evaluate the quality and diversity of the content generated by your model. You also perform validation and testing to ensure that your model meets the requirements and expectations of your goal.
- Deployment: This is where you deploy the model into the infrastructure and integrate it with the application. You also monitor and maintain the model to ensure its reliability and security.
B and D are not part of the generative AI model lifecycle mentioned in the course. B refers to manipulating the model to align with specific project needs, which is a vague and broad term that could encompass different aspects of model design and training, such as fine-tuning, pruning, or distillation. D refers to performing regularization, which is a technique used to prevent overfitting and improve generalization, but it is not a separate stage in the lifecycle, rather it is part of the model design and training stage.
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