Introduction Data Scientists have an important role in the modern machine-learning world. Leveraging ML pipelines can save them time, money, and effort and ensure that...

This post is co-authored by Tristan Miller from Best Egg. Best Egg is a leading financial confidence platform that provides lending products and resources...

Image by Author  Hyperparameter optimization plays a crucial role in determining the performance of a machine learning model. They are one the 3 components of...

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated...

Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions,...

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full...

The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and...

AutoGluon-TimeSeries is the latest addition to AutoGluon, which helps you easily build powerful time series forecasting models with as little as three lines of...

Gokul Subramanian Ravi began his CIFellowship in September 2020 after receiving his PhD (focused on computer architecture) from the  University of Wisconsin-Madison in August 2020. Gokul is currently at the University of Chicago working on quantum computing with Frederic Chong, Seymour Goodman Professor of Computer Science. Linked are his blogs on variational quantum algorithms and bringing more classical computer architects into the quantum world. Gokul is currently on the 2022-23 academic job market. The remainder of this post is written by Gokul Ravi Current Project Quantum computing is a disruptive technological paradigm with the potential to revolutionize computing, and therefore, the world. Over three decades, the promise of quantum computing […]

This technical paper titled “DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks” is co-authored from researchers at The University...