Guiding machine learning models and related components
Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.
1. A system comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a suggestion component that receives a user query for suggested models, wherein the user query comprises model-related data;
an identification component that identifies models, wherein the models respectively comprise model components;
a learning component trains a cognitive exploration system comprising a neural network to determine model relations among the model components and the model-related data;
a determination component that determines, using the cognitive exploration system comprising the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise vectors of component relations between respective pairwise ones of the model components and the model-related data; and
the suggestion component responds to the user query with the suggested models comprising a subset of the models selected, using the cognitive exploration system comprising the neural network, based on the vectors of component relations, wherein the subset of the models are selected to be most dissimilar to the model-related data according to a tolerance parameter indicative of degree of similarity.
2. The system of claim 1 , wherein the computer executable components further comprise:
a distance component that computes distances between the pairwise ones of the model components and the model-related data.
3. The system of claim 2 , wherein the subset of the models are selected based further on the distances as represented in the vectors of component relations.
4. The system of claim 3 , wherein the subset of the models are selected based further on a comparison of the distances to the tolerance parameter.
5. The system of claim 1 , wherein the subset of the models are selected to be similar to the model-related data according to another tolerance parameter indicative of degree of similarity with respect to a first criterion, and are selected to be most dissimilar to the model-related data according to the tolerance parameter indicative of degree of similarity with respect to a second criterion.
6. The system of claim 1 , wherein the model components comprise at least one of model configurations, model program code, model training data, model feedback, deployment data, or parent model information.
7. A computer-implemented method comprising:
receiving, by a device operatively coupled to a processor, a user query for suggested models, wherein the user query comprises model-related data;
identifying, by the device, models, wherein the models respectively comprise model components;
training, by the device, a cognitive exploration system comprising a neural network to determine model relations among the model components and the model-related data;
determining, by the device, using the cognitive exploration system comprising the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data; and
responding, by the device, to the user query with the suggested models comprising a subset of the models selected, using the cognitive exploration system comprising the neural network, based on the vectors of component relations, wherein the subset of the models are selected to have a high dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.
8. The computer-implemented method of claim 7 , wherein the subset of the models are selected to have a high similarity to the model-related data according to another tolerance parameter indicative of degree of similarity with respect to a first criterion, and are selected to have the high dissimilarity to the model-related data according to the tolerance parameter indicative of degree of similarity with respect to a second criterion.
9. The computer-implemented method of claim 7 , further comprising:
computing, by the device, distances between the pairwise ones of the model components and the model-related data.
10. The computer-implemented method of claim 9 , further comprising selecting, by the device, using the cognitive exploration system comprising the neural network, the subset of the models based on the distances as represented in the vectors of component relations.
11. The computer-implemented method of claim 10 , wherein the selecting is further based on a comparison of the distances to the tolerance parameter.
12. The computer-implemented method of claim 7 , wherein the model components comprise at least one of model configurations, model program code, model training data, model feedback, deployment data, or parent model information.
13. A computer program product for providing guidance in machine learning models, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
receive a user query for suggested models, wherein the user query comprises model-related data;
identify models, wherein the models respectively comprise model components;
train a cognitive exploration system comprising a neural network to determine model relations among the model components and the model-related data;
determine, using the cognitive exploration system comprising the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data; and
respond to the user query with the suggested models comprising a subset of the models selected, using the cognitive exploration system comprising the neural network, based on the vectors of component relations, wherein the subset of the models are selected to be of greater dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.
14. The computer program product of claim 13 , wherein the subset of the models are selected to be of greater similarity to the model-related data according to another tolerance parameter indicative of degree of similarity with respect to a first criterion, and are selected to be of the greater dissimilarity to the model-related data according to the tolerance parameter indicative of degree of similarity with respect to a second criterion.
15. The computer program product of claim 13 , wherein the program instructions further cause the processor to:
compute distances between the pairwise ones of the model components and the model-related data.
16. The computer program product of claim 15 , wherein the program instructions further cause the processor to:
select, using the cognitive exploration system comprising the neural network, the subset of the models based on the distances as represented in the vectors of component relations.
17. The computer program product of claim 13 , wherein the model components comprise at least one of model configurations, model program code, model training data, model feedback, deployment data, or parent model information.
18. A system comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a suggestion component that receives a user request for suggested models, wherein the user request comprises model-related data, and the model-related data comprises a training data set and specifies a type of problem to be solved;
an identification component that identifies models, wherein the models respectively comprise model components;
a learning component trains a neural network to determine model relations among the model components and the model-related data;
a determination component that determines, using the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise vectors of component relations between respective pairwise ones of the model components and the model-related data; and
the suggestion component responds to the user request with the suggested models comprising a subset of the models that have been previously employed for the type of problem to be solved and are selected, using the neural network, based on the vectors of component relations, wherein the subset of the models are selected to be most dissimilar to the model-related data according to a tolerance parameter indicative of degree of similarity.
19. The system of claim 18 , wherein the computer executable components further comprise:
a distance component that computes distances between the pairwise ones of the model components and the model-related data.
20. The system of claim 19 , wherein the subset of the models are selected based further on the distances as represented in the vectors of component relations.
21. The system of claim 20 , wherein the subset of the models are selected based further on a comparison of the distances to the tolerance parameter.
22. A computer-implemented method comprising:
receiving, by a device operatively coupled to a processor, a user request for suggested models, wherein the user request comprises model-related data, and the model-related data comprises a training data set and specifies a type of problem to be solved;
identifying, by the device, models, wherein the models respectively comprise model components;
training, by the device, a neural network to determine model relations among the model components and the model-related data;
determining, by the device, using the neural network, the model relations among the model components and the model-related data, wherein the model relations comprise respective vectors of component relations between respective pairwise ones of the model components and the model-related data; and
responding, by the device, to the user request with the suggested models comprising a subset of the models that have been previously employed for the type of problem to be solved and are selected, using the neural network, based on the vectors of component relations, wherein the subset of the models are selected to have a high dissimilarity to the model-related data according to a tolerance parameter indicative of degree of similarity.
23. The computer-implemented method of claim 22 , further comprising:
computing, by the device, distances between the pairwise ones of the model components and the model-related data.
24. The computer-implemented method of claim 23 , further comprising selecting, by the device, using the neural network, the subset of the models based on the distances as represented in the vectors of component relations.
25. The computer-implemented method of claim 24 , wherein the selecting is further based on a comparison of the distances to the tolerance parameter.