Videos

SigOpt ML Engineer Meghana Ravikumar shares how concurrently tuning metrics like model accuracy and number of model parameters allows us to distill BERT and assess the trade-offs between model size and performance.

SigOpt is designed to accelerate hyperparameter optimization by intelligently trading off exploration and exploitation with an ensemble of Bayesian and global optimization algorithms that do not require access to proprietary prior insights on a model or parameters. But our users often have deep insights on their models and parameters that could guide this tuning process. Michael McCourt, Head of Research, walks through how SigOpt's newest feature Prior Beliefs to incorporated this type of prior knowledge into SigOpt’s hyperparameter optimization process.

SigOpt provides a “human-in-the-loop” process so that you can guide the platform’s discovery of a more effective set of parameters for your model, based on external business factors or domain-specific expertise. This process is called Metric Management, and is enabled by a variety of tools built into our API and web interface.

In deep learning, it can be particularly tough to select the right metric and know when a model has converged during training. In this talk, we discuss ways to monitor convergence, automate early stopping and set the right metric strategy for deep learning training and tuning jobs

SigOpt ML Engineer Tobias Andreasen shares how to efficiently tune deep learning models by using asynchronous parallelization and multitask.

Modelers from Yelp, NVIDIA, Alectio and Pandora share tips for modeling remotely.

How can resource-constrained teams make tradeoffs between efficiency and effectiveness using pre-trained models?

To kick off our first session of Tuning for Systematic Trading, SigOpt ML Engineer Tobias Andreasen walks us through an introduction of Bayesian optimization and some advanced features that can help you optimize model performance.

SigOpt ML Engineer Meghana Ravikumar shares her work exploring tradeoffs between transfer learning methods, optimization strategies, and automated data augmentation.

Abstract: In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques.

SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.

This webinar discusses three of SigOpt's advanced optimization techniques.

Three lessons to help any enterprise scale their modeling process

Building on the “Best Practices for Hyperparameter Tuning with MLflow” talk, we will present advanced topics in HPO for deep learning, including early stopping, multi-metric optimization, and robust optimization.

SigOpt works with trading firms who represent $300B in AUM. This talk draws lessons from these engagements to provide insights on how to model at scale.

Matt Greenwood, CIO of Two Sigma and Scott Clark, SigOpt CEO, discuss best practices for modeling at scale at O'Reilly AI

Alexandra Johnson discusses techniques for overcoming cluster management challenges in distributed training.

SigOpt presented a case study on optimizing expensive functions at GTC SJ in 2019.

SigOpt provided an overview of technology that makes it easy to optimize in parallel at GTC SJ in 2019.

Patrick Hayes originally gave this talk at ODSC West in 2018.

During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.

SigOpt CEO Scott Clark explains the value of multimetric optimization, a new capability embedded within the SigOpt Optimization Platform.

Join us as we explore how SigOpt is using AWS to optimize machine learning and AI pipelines.

Sneak away and catch up with Scott Clark, Co-Founder and CEO of Sigopt, a company whose software is focused on automatically tuning your model’s parameters through Bayesian optimization.

Learn how you can use SigOpt to optimize your trading models.

Learn how you can use SigOpt to optimize your AI and Machine Learning models.

Short tutorial on how to use SigOpt, AWS and TensorFlow to efficiently build a convolutional neural network for classifying digits in the SVHN dataset.

Common Problems In Hyperparameter Optimization: All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters.

During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.

In this video I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.

Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive.

There are so many exciting and new ideas to make beautiful in machine learning.

In this video, we will walk through an example that uses SigOpt to tune a machine learning pipeline for a classification task.

In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive.

Short tutorial on how to use SigOpt, AWS and our scikit-learn wrapper to quickly optimize the hyperparameters, train and evaluate several classification models on a given dataset.

Bayesian Optimization methods used by SigOpt, coupled with the incredibly scalable deep learning architecture provided with the Nervana Cloud and neon, allow anyone to easily tune their models to quickly achieve higher accuracy.