As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

Nowadays, machine learning projects have become more and more relevant to various real-
world use cases. The success of complex Neural Network models depends upon many …

Graph structured data are abundant in the real world. Among different graph types, directed
acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data
scientists and domain experts (eg, data exploration, model training, etc.). Only till recently …

Many decision-making processes have begun to incorporate an AI element, including prison
sentence recommendations, college admissions, hiring, and mortgage approval. In all of …

We present Amazon SageMaker Autopilot: a fully managed system that provides an
automatic machine learning solution. Given a tabular dataset and the target column name …