AI Use Cases and Workflows in Cloud Deployments

These flashcards cover key concepts, workflows, and use cases related to AI implementations in cloud environments as discussed in the lecture.

What is a critical factor for optimizing efficiency and performance of AI solutions in the cloud?

Effective workload and infrastructure management.

What does cloud-based model deployment management enable?

It enables organizations to deploy machine learning models at scale, automate processes, and manage model versions.

What are cloud native management and orchestration tools used for?

They are used to manage and orchestrate cloud-based applications and services.

What do cloud-based cluster management solutions help organizations achieve?

They help organizations deploy, manage, and scale containerized applications across distributed cloud environments.

What benefit do infrastructure acceleration libraries provide?

They provide pre-built components and tools for rapid deployment and scaling of cloud-based applications.

Why is cloud deployment ideal for AI development activities?

Because developing and training AI models are resource-intensive, and cloud resources provide needed scalability and flexibility.

What is data preparation in the context of machine learning?

It is the process of preparing raw data to make it suitable for machine learning models.

What is modeling training?

It is teaching AI to accurately interpret and learn from data to perform a task with accuracy.

What happens during the simulate and test phase in machine learning?

You iteratively improve model accuracy to reduce error.

What are some common AI use cases for cloud deployment?

Large language models, speech AI applications, recommendation engines, fraud detection, sentiment analysis, and predictive maintenance.

What is sentiment analysis?

It is the process of using natural language processing and machine learning to identify and quantify emotional tone in text data.