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Modeling Best Practices

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Using SigOpt's XGBoost Integration for Regression

Using SigOpt's XGBoost Integration for Fraud Classification

Experimental Design in Materials Science

Guide to Iteratively Tuning GNNs

ICYMI – Recap: Intelligent Experimentation Overview for Ai4 Webinar

Webinar in review: MLOps to reduce friction, technical debt, and modeling

Model Optimization at the Center of MLOps

How SigOpt can Anchor your MLOps Strategy

Store Visual Artifacts in SigOpt to See the Bigger Modeling Picture

Runs: An Easy Way to Track Training

Tips for Tracking & Analyzing Training Runs

Bayesian Optimization 101

5 Signs You Need to Invest in Hyperparameter Optimization

The Latest In ML Ops - 5 Evolutions of Production ML

Why is Experiment Management Important for NLP?

A Comparison of Bayesian Packages for Hyperparameter Optimization

Efficient BERT: Find your optimal model with Multimetric Bayesian Optimization

Keeping track of it all: recording and organizing model training runs

Modeling Managed

ICYMI Recap: Warm Start Tuning with Prior Beliefs

ICYMI Recap: Introducing Metric Management

Applying Prior Beliefs in SigOpt

Why are Transformers important?

The Growing Abundance of Optimization in Peer-Reviewed Research

How We Scaled SigOpt to Handle the Most Relentless of Workloads: Part 2

Tuning for Systematic Trading: Efficient deep learning and tuning

ICYMI Recap: Modeling in Place—a panel discussion with Alectio, NVIDIA, Pandora, and Yelp

SigOpt Webinar: Modeling in place

SigOpt Webinar: Tuning Data Augmentation to Boost Model Performance

Our new SigOpt user experience puts insights at your fingertips

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Modeling at Scale: SigOpt at TWIMLcon 2019

Tuning 2.0: Advanced Optimization Techniques Webinar

SigOpt at Ai4 Finance: Modeling at Scale

SigOpt at Ai4 Finance: Modeling at Scale

Number of AI Projects Set to Explode, But Challenges Persist

O'Reilly AI 2019: Best practices for scaling modeling platforms

AI in the enterprise: 8 myths, debunked

Modeling at scale in systematic trading

SigOpt Webinar: Modeling at Scale in Systematic Trading

Best Practices for Scaling Modeling Platforms

Why Two Sigma is using SigOpt for Automated Parameter Tuning

SigOpt at GTC: Accelerating Model Development by Reducing Operational Barriers

Deep Learning: When Should You Use It?

AI vs. machine learning: What's the difference?

Optimizing End-to-End Memory Networks Using SigOpt and GPUs

Three Machine Learning Tasks Every AI Team Should Automate

On the Radar: SigOpt for machine learning algorithm optimization

Uncertainty and How it Impacts Modeling, Optimization, and Metrics

Visualizing Abstract Concepts in Machine Learning

Hyperparameter Optimization 101

Common Problems in Hyperparameter Optimization

Machine Learning Fundamentals

A Modern Take on the Bias-Variance Tradeoff in Neural Networks

Two Sigma & SigOpt Discussion on Modeling Best Practices

Advances in Using GPs with Derivative Observations

5 Essential Pieces of the Deep Learning Puzzle

Uncertainty and How it Impacts Modeling, Optimization and Metrics

Roadblocks to Automated Testing

Why Getting the Metrics Right Is So Important in Machine Learning

Bringing Deep Learning to the Cloud

SigOpt CEO on the art of tuning AI models and doing ‘optimization as a service’

Active Preference Learning for Personalized Portfolio Construction

Using SigOpt and Tensorflow for Convolutional Neural Networks

Common Problems in Hyperparameter Optimization

Bayesian Optimization for Machine Learning : A Practical Guidebook

Visualizing Abstract Concepts in Machine Learning

Exploiting Unlabelled Data with Bayesian Optimization

Using SigOpt and scikit-learn for machine learning

Evaluation System for a Bayesian Optimization Service

A Stratified Analysis of Bayesian Optimization Methods

Optimizing Deep Learning Models

A New Experiment List and Search Interface

Experiment, Suggestion, Observation: How We Named Our HPO API Objects

Uncertainty 3: Balancing Multiple Metrics with Uncertainty

Uncertainty 2: Bayesian Optimization with Uncertainty

Uncertainty 1: Modeling with Uncertainty

Circulant Binary Embeddings

Covariance Kernels for Avoiding Boundaries

Expected Improvement vs. Knowledge Gradient

Clustering Applied to Acquisition Functions

Dealing with Troublesome Metrics

Intuition behind Gaussian Processes

Profile Likelihood vs. Kriging Variance

Likelihood for Gaussian Processes

Automatically Tuning Text Classifiers

Approximation of Data

Intuition Behind Covariance Kernels

Picking the Right Metric