Computer-based models can be developed, deployed, and managed in an automated manner. For example, a model building tool can be selected based on the model building tool being compatible with one or more parameters. A first machine-learning model can be generated using the model building tool and trained using a training dataset. The first machine-learning model can then be used to perform a task. Thereafter, a new model-building tool can be selected based on the new model-building tool being compatible with the one or more parameters. A second machine-learning model can be generated using the new model-building tool and trained using the training dataset. The accuracy of the first machine-learning model can be compared to the accuracy of the second machine-learning model. Based on the second machine-learning model being more accurate, the second machine-learning model can be used to perform the particular task rather than the first machine-learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processing device; and a memory device on which instructions executable by the processing device are stored for causing the processing device to: select a model building tool from among a plurality of model building tools based on the model building tool being compatible with one or more parameters of (i) a machine learning model or (ii) training data usable to train the machine learning model, the plurality of model building tools being available for use in generating machine learning models; generate a first machine-learning model using the model building tool by providing the one or more parameters to the model building tool; train the first machine-leaning model using a training dataset; use the first machine-learning model to perform a particular task; and subsequent to using the first machine-learning model to perform the particular task: receive a new model-building tool; incorporate the new model-building tool into the plurality of model building tools; select the new model-building tool from among the plurality of model building tools based on the new model-building tool being compatible with the one or more parameters; generate a second machine-learning model using the new model-building tool by providing the one or more parameters to the new model-building tool; train the second machine-learning model using the training dataset; provide an input value from the training dataset to the first machine-learning model to determine a first output from the first machine-learning model; provide the input value from the training dataset to the second machine-learning model to determine a second output from the second machine-learning model; compare the first output from the first machine-learning model and the second output from the second machine-learning model to an output value in the training dataset to determine whether the first output or the second output is closer to the output value in the training dataset, the output value in the training dataset being correlated to the input value in the training dataset; and based on determining that the second output is closer to the output value in the training data than the first output, use the second machine-learning model to perform the particular task rather than the first machine-learning model. 2. The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to train the first machine-learning model and the second machine-learning model using the training dataset prior to providing the input value to the first machine-learning model and the second machine-learning model. 3. The system of claim 1, wherein the model building tool is created by a first entity and the new model-building tool is created by a second entity that is different from the first entity. 4. The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: detect a condition indicating that the second machine-learning model is to be re-trained; and re-train the second machine-learning model in response to detecting the condition. 5. The system of claim 4, wherein the condition includes a change in sentiment related to an object between a current time and a prior time at which the second machine-learning model was trained. 6. The system of claim 4, wherein the condition includes a change in an output from the second machine-learning model between a current time and a prior time at which the second machine-learning model was trained. 7. The system of claim 4, wherein the condition includes an output from the second machine-learning model deviating by at least a predetermined amount over a time period. 8. The system of claim 4, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to, prior to using the first machine-learning model to perform a particular task: select the first-machine learning model as a candidate champion model from among a plurality of models; perform a champion model approval process on the candidate champion model to determine that the candidate champion model is approved; and based on determining that the candidate champion model is approved, select the candidate champion model for use in performing the particular task. 9. The system of claim 8, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to, after selecting the candidate champion model for use in performing the particular task, publish the candidate champion model to a production environment. 10. The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to, after training the second machine-learning model: analyze an output from the second machine-learning model to determine a frequency at which to re-train the second machine-learning model; and re-train the second machine-learning model at the frequency. 11. A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to: select a model building tool from among a plurality of model building tools based on the model building tool being compatible with one or more parameters of (i) a machine learning model or (ii) training data usable to train the machine learning model, the plurality of model building tools being available for use in generating machine learning models; generate a first machine-learning model using the model building tool by providing the one or more parameters to the model building tool; train the first machine-leaning model using a training dataset; use the first machine-learning model to perform a particular task; and subsequent to using the first machine-learning model to perform the particular task: receive a new model-building tool; incorporate the new model-building tool into the plurality of model building tools; select the new model-building tool from among the plurality of model building tools based on the new model-building tool being compatible with the one or more parameters; generate a second machine-learning model using the new model-building tool by providing the one or more parameters to the new model-building tool; train the second machine-learning model using the training dataset; provide an input value from the training dataset to the first machine-learning model to determine a first output from the first machine-learning model; provide the input value from the training dataset to the second machine-learning model to determine a second output from the second machine-learning model; compare the first output from the first machine-learning model and the second output from the second machine-learning model to an output value in the training dataset to determine whether the first output or the second output is closer to the output value in the training dataset, the output value in the training dataset being correlated to the input value in the training dataset; and based on determining that the second output is closer to the output value in the training data than the first output, use the second machine-learning model to perform the particular task rather than the first machine-learning model. 12. The non-transitory computer-readable medium of claim 11, further comprising instructions executable by the processing device for causing the processing device to train the first machine-learning model and the second machine-learning model using the training dataset prior to providing the input value to the first machine-learning model and the second machine-learning model. 13. The non-transitory computer-readable medium of claim 11, wherein the model building tool is created by a first entity and the new model-building tool is created by a second entity that is different from the first entity. 14. The non-transitory computer-readable medium of claim 11; further comprising instructions executable by the processing device for causing the processing device to: detect a condition indicating that the second machine-learning model is to be re-trained; and re-train the second machine-learning model in response to detecting the condition. 15. The non-transitory computer-readable medium of claim 14, wherein the condition includes a change in sentiment related to an object between a current time and a prior time at which the second machine-learning model was trained. 16. The non-transitory computer-readable medium of claim 14, wherein the condition includes a change in an output from the second machine-learning model between a current time and a prior time at which the second machine-learning model was trained. 17. The non-transitory computer-readable medium of claim 14, wherein the condition includes an output from the second machine-learning model deviating by at least a predetermined amount over a time period. 18. The non-transitory computer-readable medium of claim 14, further comprising instructions executable by the processing device for causing the processing device to, prior to using the first machine-learning model to perform a particular task: select the first-machine learning model as a candidate champion model from among a plurality of models; perform a champion model approval process on the candidate champion model to determine that the candidate champion model is approved; and based on determining that the candidate champion model is approved, select the candidate champion model for use in performing the particular task. 19. The non-transitory computer-readable medium of claim 18, further comprising instructions executable by the processing device for causing the processing device to, after selecting the candidate champion model for use in performing the particular task, publish the candidate champion model to a production environment. 20. The non-transitory computer-readable medium of claim 11, further comprising instructions executable by the processing device for causing the processing device to, after training the second machine-learning model: analyze an output from the second machine-learning model to determine a frequency at which to re-train the second machine-learning model; and re-train the second machine-learning model at the frequency. 21. A method comprising: selecting, by a processing device, a model building tool from among a plurality of model building tools based on the model building tool being compatible with one or more parameters of (i) a machine learning model or (ii) training data usable to train the machine learning model, the plurality of model building tools being available for use in generating machine learning models; generating, by the processing device, a first machine-learning model using the model building tool by providing the one or more parameters to the model building tool; training, by the processing device, the first machine-leaning model using a training dataset; using, by the processing device, the first machine-learning model to perform a particular task; and subsequent to using the first machine-learning model to perform the particular task: receiving, by the processing device, a new model-building tool; incorporating, by the processing device, the new model-building tool into the plurality of model building tools; selecting, by the processing device, the new model-building tool from among the plurality of model building tools based on the new model-building tool being compatible with the one or more parameters; generating, by the processing device, a second machine-learning model using the new model-building tool by providing the one or more parameters to the new model-building tool; training, by the processing device, the second machine-learning model using the training dataset; providing, by the processing device, an input value from the training dataset to the first machine-learning model to determine a first output from the first machine-learning model; providing, by the processing device, the input value from the training dataset to the second machine-learning model to determine a second output from the second machine-learning model; comparing, by the processing device, the first output from the first machine-learning model and the second output from the second machine-learning model to an output value in the training dataset to determine whether the first output or the second output is closer to the output value in the training dataset, the output value in the training dataset being correlated to the input value in the training dataset; and based on determining that the second output is closer to the output value in the training data than the first output, using, by the processing device, the second machine-learning model to perform the particular task rather than the first machine-learning model. 22. The method of claim 21, further comprising training the first machine-learning model and the second machine-learning model using the training dataset prior to providing the input value to the first machine-learning model and the second machine-learning model. 23. The method of claim 21, wherein the model building tool is created by a first entity and the new model-building tool is created by a second entity that is different from the first entity. 24. The method of claim 21, further comprising: detecting a condition indicating that the second machine-learning model is to be re-trained; and re-training the second machine-learning model in response to detecting the condition. 25. The method of claim 24, wherein the condition includes a change in sentiment related to an object between a current time and a prior time at which the second machine-learning model was trained. 26. The method of claim 24, wherein the condition includes a change in an output from the second machine-learning model between a current time and a prior time at which the second machine-learning model was trained. 27. The method of claim 24, wherein the condition includes an output from the second machine-learning model deviating by at least a predetermined amount over a time period. 28. The method of claim 24, further comprising, prior to using the first machine-learning model to perform a particular task: selecting the first-machine learning model as a candidate champion model from among a plurality of models; performing a champion model approval process on the candidate champion model to determine that the candidate champion model is approved; and based on determining that the candidate champion model is approved, selecting the candidate champion model for use in performing the particular task. 29. The method of claim 28, further comprising, after selecting the candidate champion model for use in performing the particular task, publishing the candidate champion model to a production environment. 30. The method of claim 21, further comprising, after training the second machine-learning model: analyzing an output from the second machine-learning model to determine a frequency at which to re-train the second machine-learning model; and re-training the second machine-learning model at the frequency.