Videos

Efficient BERT: Find your optimal model with Multimetric Bayesian Optimization
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 Webinar: Warm Start Tuning with Prior Beliefs
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.
All Model Types, Multimetric Optimization, Training & Tuning
SigOpt Webinar: Introducing Metric Management
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.
Advanced Optimization Techniques, Deep Learning, Training & Tuning
Tuning for Systematic Trading: Training, Tuning and Metric Strategy
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
Modeling Best Practices
Tuning for Systematic Trading: Efficient deep learning and tuning
SigOpt ML Engineer Tobias Andreasen shares how to efficiently tune deep learning models by using asynchronous parallelization and multitask.
Modeling Best Practices
SigOpt Webinar: Modeling in place
Modelers from Yelp, NVIDIA, Alectio and Pandora share tips for modeling remotely.
Advanced Optimization Techniques, Data Augmentation, Deep Learning
SigOpt GTC 2020: Optimized Image Classification on the Cheap
How can resource-constrained teams make tradeoffs between efficiency and effectiveness using pre-trained models?
Advanced Optimization Techniques, All Model Types, Multimetric Optimization
Tuning for Systematic Trading: Intuition behind Bayesian optimization with and without multiple metrics
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.
Advanced Optimization Techniques, Data Augmentation, Deep Learning, Modeling Best Practices
SigOpt Webinar: Tuning Data Augmentation to Boost Model Performance
SigOpt ML Engineer Meghana Ravikumar shares her work exploring tradeoffs between transfer learning methods, optimization strategies, and automated data augmentation.
Advanced Optimization Techniques, Data Augmentation, Deep Learning
SigOpt at MLconf: Optimized Image Classification on the Cheap
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.
Advanced Optimization Techniques, Machine Learning, Multimetric Optimization
SigOpt Webinar: Critical Capabilities of Optimization in the Enterprise
SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.
Advanced Optimization Techniques, All Model Types, Deep Learning, Training & Tuning
Tuning 2.0: Advanced Optimization Techniques Webinar Recording
This webinar discusses three of SigOpt's advanced optimization techniques.
All Model Types, Modeling Best Practices
SigOpt at Ai4 Finance: Modeling at Scale
Three lessons to help any enterprise scale their modeling process
Deep Learning, Training & Tuning
Advanced Hyperparameter Optimization for Deep Learning with MLflow
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.
All Model Types, Modeling Best Practices
SigOpt Webinar: Modeling at Scale in Systematic Trading
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.
All Model Types, Modeling Best Practices
Best Practices for Scaling Modeling Platforms
Matt Greenwood, CIO of Two Sigma and Scott Clark, SigOpt CEO, discuss best practices for modeling at scale at O'Reilly AI
Augmented ML Workflow, Deep Learning
SigOpt at MLconf: Reducing Operational Barriers to Model Training
Alexandra Johnson discusses techniques for overcoming cluster management challenges in distributed training.
Advanced Optimization Techniques, Deep Learning
SigOpt at GTC: Tuning the Untunable – Lessons for Deep Learning Optimization
SigOpt presented a case study on optimizing expensive functions at GTC SJ in 2019.
All Model Types, Modeling Best Practices
SigOpt at GTC: Accelerating Model Development by Reducing Operational Barriers
SigOpt provided an overview of technology that makes it easy to optimize in parallel at GTC SJ in 2019.
Advanced Optimization Techniques, Deep Learning
Tuning the Untunable: Insights for Deep Learning Optimization
Patrick Hayes originally gave this talk at ODSC West in 2018.
All Model Types, Modeling Best Practices
Two Sigma & SigOpt Discussion on Modeling Best Practices
During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.
Advanced Optimization Techniques, Deep Learning
Applying Multimetric Optimization to Solve Business Problems
SigOpt CEO Scott Clark explains the value of multimetric optimization, a new capability embedded within the SigOpt Optimization Platform.
Deep Learning, Modeling Best Practices
Bringing Deep Learning to the Cloud
Join us as we explore how SigOpt is using AWS to optimize machine learning and AI pipelines.
All Model Types, Training & Tuning
Bayesian Optimization for Hyperparameter Tuning
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.
Applied AI Insights, Simulations & Backtests
SigOpt for Trading
Learn how you can use SigOpt to optimize your trading models.
Applied AI Insights, Machine Learning
SigOpt for AI
Learn how you can use SigOpt to optimize your AI and Machine Learning models.
Deep Learning, Modeling Best Practices
Using SigOpt and Tensorflow for Convolutional Neural Networks
Short tutorial on how to use SigOpt, AWS and TensorFlow to efficiently build a convolutional neural network for classifying digits in the SVHN dataset.
All Model Types, Modeling Best Practices
Common Problems in Hyperparameter Optimization
Common Problems In Hyperparameter Optimization: All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters.
Applied AI Insights, Simulations & Backtests
SigOpt for Algorithmic Trading
During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.
Applied AI Insights, Simulations & Backtests
SigOpt for Hedge Funds
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.
Machine Learning, Training & Tuning
Using Optimal Learning to Tune Machine Learning Models
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.
Machine Learning, Modeling Best Practices
Visualizing Abstract Concepts in Machine Learning
There are so many exciting and new ideas to make beautiful in machine learning.
Machine Learning, Modeling Best Practices
Exploiting Unlabelled Data with Bayesian Optimization
In this video, we will walk through an example that uses SigOpt to tune a machine learning pipeline for a classification task.
Machine Learning, Training & Tuning
Optimizing 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.
Machine Learning, Modeling Best Practices
Using SigOpt and scikit-learn for machine learning
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.
Deep Learning, Modeling Best Practices
Optimizing Deep Learning Models
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.