Abstract: Artificial Intelligence (AI) may be used to generate insights to assist in building, debugging, and deploying AI models. In one or more embodiments, a user (e.g., AI developer) may pose a question (e.g., in a user interface), and a router and planner may wrap the question with AI model data and guidelines to send to an LLM. Based on the complex input—i.e., not just the user-posed question—the LLM based answer may provide additional insights and information that may not be readily apparent to the user. In one or more embodiments, the performance (e.g., drift) of the AI model may be tracked over time, and such tracking may be used to generate deeper answers through cognitive analysis. The answers may be displayed back on the user interface. Therefore, the embodiments herein can be leveraged as a co-pilot when developing AI models.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.

Abstract: Techniques for determining a drift impact score in a machine learning model are disclosed. The techniques can include: obtaining a reference distribution of a machine learning model; obtaining a current distribution of the machine learning model; determining a statistical distance based on the reference distribution and the current distribution; determining a local feature importance parameter for each feature associated with a prediction made by the machine learning model; determining a cohort feature importance parameter for a cohort of multiple features based on the local feature importance parameter of each feature in the cohort; and determining a drift impact score for the cohort based on the statistical distance and the cohort feature importance parameter.

Abstract: Techniques for optimizing a machine learning model. The techniques may include obtaining multiple predictions from a machine learning model, the predictions being based on at least one input feature vector, each input feature vector having one or more vector values; creating at least one slice of the predictions based on at least one vector value; determining a sensitive bias metric for the slice based on a sensitive group; determining a base metric for the slice based on a base group; determining a parity metric for the slice based on a ratio of the sensitive bias metric and the base metric; and optimizing the machine learning model based on the parity metric.

Abstract: Techniques for optimizing a machine learning model. The techniques can include: obtaining one or more embedding vectors based on a prediction of a machine learning model; mapping the embedding vectors from a higher dimensional space to a 2D/3D space to generate one or more high density points in the 2D/3D space; clustering the high-density points by running a clustering algorithm multiple times, each time with a different set of parameters to generate one or more clusters; applying a purity metric to each cluster to generate a normalized purity score of each cluster; identifying one or more clusters with a normalized purity score lower than a threshold; and optimizing the identifying one or more clusters.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.

Abstract: Techniques for determining a drift impact score in a machine learning model are disclosed. The techniques can include: obtaining a reference distribution of a machine learning model; obtaining a current distribution of the machine learning model; determining a statistical distance based on the reference distribution and the current distribution; determining a local feature importance parameter for each feature associated with a prediction made by the machine learning model; determining a cohort feature importance parameter for a cohort of multiple features based on the local feature importance parameter of each feature in the cohort; and determining a drift impact score for the cohort based on the statistical distance and the cohort feature importance parameter.

Abstract: Techniques for determining embedding drift score in a machine learning model. The techniques can include: obtaining one or more first embedding vectors based on at least one first prediction of a machine learning model; filtering the first embedding vectors based on a slice of the first prediction; determining a first average vector by averaging each dimension of the filtered first embedding vectors; obtaining one or more second embedding vectors on at least one second prediction of the machine learning model; filtering the second embedding vectors based on a slice of the second prediction; generating a second average vector by averaging each dimension of the filtered second embedding vectors; and determining an embedding drift score based on a distance measure of the first average vector and the second average vector.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.