AI ML Use Cases Flashcards
IDP
is an application that extracts and classifies information from unstructured data, generates summaries, and provides actionable insights.
AWS HealthScribe:
This AWS service empowers healthcare software vendors to build clinical applications that automatically generate clinical notes by analyzing patient-clinician conversations.
AI is a good choice for the following use cases:
Coding the rules is challenging
Scale of the project is challenging
Two Types of Machine Learning Supervised Learning
Classification
Regression
Classification is
a supervised learning technique used to assign labels or categories to new, unseen data instances based on a trained model. The model is trained on a labeled dataset, where each instance is already assigned to a known class or category. The goal of classification is to learn patterns from the training data and use them to predict the class or category for new unlabeled data instances.
Regression is a
supervised learning technique used for predicting continuous or numerical values based on one or more input variable. It is used to model the relationship between a dependent variable (the value to be predicted) and one or more independent variables (the features or inputs used for prediction).
Unsupervised learning
n this type of learning, the machine has to uncover and create the labels itself. These models use the data they’re presented with to detect emerging properties of the entire dataset and then construct patterns.
Two types of unsupervised learning
Clustering
Dimentional Reduction
Clustering
This kind of algorithm groups data into different clusters based on similar features or distances between the data point to better understand the attributes of a specific cluster.
For example, by analyzing customer purchasing habits, an unsupervised algorithm can identify a company as being large or small.
Dimensionality reduction
is an unsupervised learning technique used to reduce the number of features or dimensions in a dataset while preserving the most important information or patterns.
Reinforcement learning
this one continuously improves its model by mining feedback from previous iterations. In reinforcement learning, an agent continuously learns through trial and error as it interacts in an environment. Reinforcement learning is broadly useful when the reward of a desired outcome is known, but the path to achieving it isn’t—and that path requires a lot of trial and error to discover.
Business Metrics User satisfaction
User satisfaction gathers user feedback to assess their satisfaction with the AI-generated content or recommendations.
Average revenue per user
Average revenue per user (ARPU) calculates the average revenue generated per user or customer attributed to the generative AI application.
Cross-domain performance
Cross-domain performance measures the generative AI model’s ability to perform effectively across different domains or industries.
Conversion rate
Conversion rate monitors the conversion rate to generate content or recommend desired outcomes, such as purchases, sign-ups, or engagement metrics.
