Abstract: A system comprising: a skills data store; an employee action data store; at least one hardware processor; and one or more software modules that are configured to, when executed by the at least one hardware processor, retrieve skills data and employee action data from the skills data store and employee action data store, train a classification model, wherein training a classification model comprises performing feature preprocessing, generating an LDA topic vector and TF/IDF Word2Vec similarity scoring, and use AutoML to train ML models, and infer employee skills and levels based on the classification model and employee action data.

Abstract: A system comprising: a skills data store; an employee action data store; at least one hardware processor; and one or more software modules that are configured to, when executed by the at least one hardware processor, retrieve skills data and employee action data from the skills data store and employee action data store, train a classification model, wherein training a classification model comprises performing feature preprocessing, generating an LDA topic vector and TF/IDF Word2Vec similarity scoring, and use AutoML to train ML models, and infer employee skills and levels based on the classification model and employee action data.

Abstract: Optimization process for automated machine learning with a combination of different optimizers. In an embodiment, optimization is performed by, for each of a plurality of machine-learning algorithms, executing a Bayesian optimization algorithm to produce a plurality of trialed models, wherein each of the plurality of trialed models is associated with the machine-learning algorithm and a set of hyperparameters. A subset of best-performing machine-learning algorithms is selected, and, for each machine-learning algorithm in the subset, a best-performing model from the plurality of trialed models associated with that machine-learning algorithm is selected, and a local search algorithm is executed starting from the set of hyperparameters associated with the selected best-performing model to identify an improved model that has better performance than the selected best-performing model.

Abstract: Time-based and accuracy-estimate-based trial selection for selection of subsequent machine-learning models in automated machine learning. In an embodiment, a batch of trials are generated for a plurality of machine-learning algorithms. During execution of the trials, a training time is estimated for at least a portion of the models represented in the batch of trials, and a subset of models are selected for evaluation based, at least in part, on their estimated training times. A graphical user interface is updated to reflect the evaluation results of the subset of models, even before the evaluation results for other models become available.