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The Next Battleground for Deep Learning Performance

The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization.
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On the Radar: SigOpt for machine learning algorithm optimization

The potential for machine learning systems, such as those based on deep learning, depends on organizations having the skill to develop their models and fine tune the multitude of model configuration parameters (known as hyperparameters).

News Articles

HFS Hot Vendors Q2 2019
AI’s carbon footprint will be an issue for enterprises
Insights for Building High-Performing Image Classification Models
AI in the enterprise: 8 myths, debunked
Why Two Sigma is using SigOpt for Automated Parameter Tuning
The insideBIGDATA IMPACT 50 List for Q2 2019
Field Report: GPU Technology Conference 2019 #GTC19
Should you build or buy your data science platform?
Deep Learning: When Should You Use It?
SigOpt Partners with Two Sigma
AI vs. machine learning: What’s the difference?
How to select an AI pilot project: 5 criteria
Optimizing End-to-End Memory Networks Using SigOpt and GPUs
How to identify an AI opportunity: 5 questions to ask
CB Insights: Here are the top 100 AI companies in the world
AI 100: The Artificial Intelligence Startups Redefining Industries
Enterprises are White Knights in Battle to Avoid AI Winter
Interview with Nick Payton, B2B Marketing Leader at SigOpt
“Above the Trend Line” – Your Industry Rumor Central for 1/14/2019
Three Machine Learning Tasks Every AI Team Should Automate
Get Ahead of Automated Machine Learning (AUTOML) to Accelerate your AI Roadmap
Let’s talk Bayesian optimization
Four ways artificial intelligence can impact the workplace and society
How machine learning will affect IT operations and careers
CNBC unveils its annual list of 100 promising start-ups to watch
Optimization: The Secret Weapon of AI Modeling at Scale
Five Attributes of Artificial Intelligence Leaders
SigOpt Automated Model Tuning: Product Overview and Analysis
21 Books Highly Recommended by Successful Executives
The insideBIGDATA IMPACT 50 List for Q3 2018
On the Radar: SigOpt for machine learning algorithm optimization
Odds & Ends: Amazon’s AI Scare, Encrypted Phones, A ‘Lost’-Inspired Startup
Roadblocks to Automated Testing
Integrating with Amazon SageMaker: Using Built-In Algorithms from External Applications
Why Getting the Metrics Right Is So Important in Machine Learning
SigOpt Within In-Q-Tel’s Parameters
SigOpt Within In-Q-Tel’s Parameters
In-Q-Tel invests in SigOpt to help U.S. intelligence agencies optimize AI
5 Essential Pieces of the Deep Learning Puzzle
New AWS Solution Space Launches with 10 Customer-Ready Solutions
The Machine Learning Opportunity in Manufacturing, Logistics
Machine Learning for the Masses
5 Questions with SigOpt’s Scott Clark
AWS Launches New Cybersecurity Services
AWS PrivateLink Now Supports Non-Amazon Services
What’s Keeping Deep Learning In Academia From Reaching Its Full Potential?
How Building the Perfect AI Pipeline Is Like Brewing the Perfect Shot of Espresso
SigOpt is Now Available on AWS Marketplace
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Tuning Machine Learning Models
If Machine Learning is the question, open source is the answer. Right?
Deep Learning Hyperparameter Optimization with Competing Objectives
Getting Hyped for Deep Learning Configs
Fast CNN Tuning with AWS GPU Instances and SigOpt
The Next Battleground for Deep Learning Performance
This ex-Yelp engineer just raised $6 million to help make tastier beer
Optimization startup SigOpt raises $6.6M
SigOpt
SigOpt Raises $6.6 Million in Series A Funding
Andreessen Horowitz backs $6.6 million round for optimization startup SigOpt
Much Deeper, Much Faster: Deep Neural Network Optimization with SigOpt and Nervana Cloud
SigOpt Raises $2 Million to ‘Optimize Anything’ From Jet Engines to Snacks
YC-Backed SigOpt Helps Customers Optimize Everything From Online Ads To Shaving Cream

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We contribute dozens of technical blog posts, academic papers, industry trend analysis articles and topical interviews to third party publications each year. We welcome additional opportunities to contribute to this broader community that cuts across AI, ML and optimization.

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