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UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty PARAMETRIZING DATA AUGMENTATION IN COVID-NET UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
HIGHLIGHT: AUTOMATING BAYESIAN OPTIMIZATION WITH Automated Bayesian optimization will place the next observation at the optimum (highest function value). In the figure above, we show two instances of Bayesian optimization where our goal is to maximize the (unknown-to-the-methods) red objective function. Both instances use expected improvement as acquisition function. THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORS KRIGING VARIANCE: IMPROVEMENT VS. KNOWLEDGE GRADIENT Let us also assume the variance of the observation is 0.5 across all options and that the best incumbent outcome is 9.5. Computing the KG and EI values using these numbers, we can compare the results in Figure 3. Figure 3. The KG and EI values for the five options (inAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } } SIGOPTSOLUTIONWHY OPTIMIZEFOR ENTERPRISEFOR ACADEMIADOCSPRODUCT SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure. To get started: Instrument your model code to track runs and model artifacts. Automate sample-efficient hyperparameter optimization. UNCERTAINTY 2: BAYESIAN OPTIMIZATION WITH UNCERTAINTY COMMON PROBLEMS IN HYPERPARAMETER OPTIMIZATION Hyperparameter optimization is a powerful tool for unlocking the maximum potential of your model, but only when it is correctly implemented. Here, we are going to share seven common problems we’ve seen while executing hyperparameter optimization. #1 Trusting theDefaults.
UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty PARAMETRIZING DATA AUGMENTATION IN COVID-NET UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
HIGHLIGHT: AUTOMATING BAYESIAN OPTIMIZATION WITH Automated Bayesian optimization will place the next observation at the optimum (highest function value). In the figure above, we show two instances of Bayesian optimization where our goal is to maximize the (unknown-to-the-methods) red objective function. Both instances use expected improvement as acquisition function. THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORS KRIGING VARIANCE: IMPROVEMENT VS. KNOWLEDGE GRADIENT Let us also assume the variance of the observation is 0.5 across all options and that the best incumbent outcome is 9.5. Computing the KG and EI values using these numbers, we can compare the results in Figure 3. Figure 3. The KG and EI values for the five options (inAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } }SOLUTION | SIGOPT
562-7-SIGOPT. sales@sigopt.com. Contact Us. Solution. Empower Your Experts. SigOpt is a model optimization and experimentation solution uniquely designed to augment your experts. Your researchers bring their expertise to bear on the data and features, whileCONTACT | SIGOPT
562-7-SIGOPT (562-774-4678) contact@sigopt.com • sales@sigopt.com. 100 Bush Street, Suite 1100, San Francisco , CA 94104. Get Directions. Modeling is messy. Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join SigOpt to get free access to these features today. For a limited time, we are offering EXPERIMENT MANAGEMENT 562-7-SIGOPT. sales@sigopt.com. Contact Us. Beta. Experiment Management. Track training runs with a few lines of code. Customize plots in an interactive dashboard. Automate hyperparameter optimization. Compatible with any library, stack, or codingenvironment.
PARAMETRIZING DATA AUGMENTATION IN COVID-NET At SigOpt, we are thrilled to collaborate with the outstanding community of experts from around the world. In this post, we discuss a recent collaboration with Linda Wang at the Vision and Image Processing Lab (VIP Lab) at the University of Waterloo. During recent months, Linda has been working with DarwinAI Corp. to develop COVID-Net: A Tailored Deep Convolutional Neural Network Design for INTRO TO MULTICRITERIA OPTIMIZATION 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / Intro to Multicriteria Optimization. In this post, we will discuss the topic of multicriteria optimization, when you need to optimize a model for more than a single metric, and how to use SigOpt to solve these problems. Here at SigOpt, we help clients to more quickly tune financial modelsand
A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
INTUITION BEHIND GAUSSIAN PROCESSES 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / Intuition behind Gaussian Processes. If you own an oil company, your job is to drill for as much oil as possible while minimizing costs. Since the primary cost involves drilling the holes, your goal is to retrieve the maximum amount of oil per hole drilled. How, then, can you predict where the HIGHLIGHT: AUTOMATING BAYESIAN OPTIMIZATION WITH Automated Bayesian optimization will place the next observation at the optimum (highest function value). In the figure above, we show two instances of Bayesian optimization where our goal is to maximize the (unknown-to-the-methods) red objective function. Both instances use expected improvement as acquisition function. THE INTUITION BEHIND COVARIANCE FUNCTION KERNELS This proper combination, which gives the best prediction at unobserved locations, is defined by a weighted average of the covariance values between observed and unobserved locations. The weighted average is determined with the help of the observed values; the derivation is a bit involved, but can be found here or here, or in a future post ofours.
METRIC STRATEGY
Metric Strategy. By default, SigOpt will aim to optimize your metric or search for the efficient frontier when more than one metric is being optimized. However, it can also be helpful to track metrics even when they are not being optimized. For example, some metrics may be very strongly correlated so you want to focus optimization on one. SIGOPTSOLUTIONWHY OPTIMIZEFOR ENTERPRISEFOR ACADEMIADOCSPRODUCT SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure. To get started: Instrument your model code to track runs and model artifacts. Automate sample-efficient hyperparameter optimization. UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty UNCERTAINTY 2: BAYESIAN OPTIMIZATION WITH UNCERTAINTY A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
INTUITION BEHIND GAUSSIAN PROCESSES 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / Intuition behind Gaussian Processes. If you own an oil company, your job is to drill for as much oil as possible while minimizing costs. Since the primary cost involves drilling the holes, your goal is to retrieve the maximum amount of oil per hole drilled. How, then, can you predict where the COMMON PROBLEMS IN HYPERPARAMETER OPTIMIZATION Hyperparameter optimization is a powerful tool for unlocking the maximum potential of your model, but only when it is correctly implemented. Here, we are going to share seven common problems we’ve seen while executing hyperparameter optimization. #1 Trusting theDefaults.
UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORSMEGHANA RAVIKUMAR
Meghana has worked with machine learning in academia and in industry, and is happiest working on natural language processing. Prior to SigOpt, she worked in biotech, employing NLP to mine and classify biomedical literature. When she’s not reading papers, developing models/tools, or trying to explain complicated topics, she enjoys doing yoga, traveling, and hunting forAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } } SIGOPTSOLUTIONWHY OPTIMIZEFOR ENTERPRISEFOR ACADEMIADOCSPRODUCT SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure. To get started: Instrument your model code to track runs and model artifacts. Automate sample-efficient hyperparameter optimization. UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty UNCERTAINTY 2: BAYESIAN OPTIMIZATION WITH UNCERTAINTY A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
INTUITION BEHIND GAUSSIAN PROCESSES 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / Intuition behind Gaussian Processes. If you own an oil company, your job is to drill for as much oil as possible while minimizing costs. Since the primary cost involves drilling the holes, your goal is to retrieve the maximum amount of oil per hole drilled. How, then, can you predict where the COMMON PROBLEMS IN HYPERPARAMETER OPTIMIZATION Hyperparameter optimization is a powerful tool for unlocking the maximum potential of your model, but only when it is correctly implemented. Here, we are going to share seven common problems we’ve seen while executing hyperparameter optimization. #1 Trusting theDefaults.
UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORSMEGHANA RAVIKUMAR
Meghana has worked with machine learning in academia and in industry, and is happiest working on natural language processing. Prior to SigOpt, she worked in biotech, employing NLP to mine and classify biomedical literature. When she’s not reading papers, developing models/tools, or trying to explain complicated topics, she enjoys doing yoga, traveling, and hunting forAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } }PRODUCT | SIGOPT
Our optimization amplifies model performance and accelerates model development. SigOpt was founded to empower the world’s experts by building software solutions that accelerate and amplify the impact of their machine learning, deep learning, and simulation models.RESOURCES | SIGOPT
Recurrent Neural Network. Modeling is messy. Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join SigOpt to get free access to these features today. For a limited time, we are offering free access to our complete product, including hyperparameter optimization. Sign up for free access here.RESEARCH | SIGOPT
Our research team is constantly developing new optimization techniques for real-world problems. With a desire for optimal outcomes or results, at SigOpt, we understand that a drive for excellence demands excellence on our part, and we have invested heavily in our research team to meet that challenge.CONTACT | SIGOPT
562-7-SIGOPT (562-774-4678) contact@sigopt.com • sales@sigopt.com. 100 Bush Street, Suite 1100, San Francisco , CA 94104. Get Directions. Modeling is messy. Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join SigOpt to get free access to these features today. For a limited time, we are offering EXPERIMENT MANAGEMENT 562-7-SIGOPT. sales@sigopt.com. Contact Us. Beta. Experiment Management. Track training runs with a few lines of code. Customize plots in an interactive dashboard. Automate hyperparameter optimization. Compatible with any library, stack, or codingenvironment.
FINANCE & INSURANCE
562-7-SIGOPT. sales@sigopt.com. Contact Us. For Enterprise. Finance & Insurance. Amplify the impact of machine learning on risk, fraud, lending and actuarial modeling with the world’s most advanced model optimization solution. Try It. Amplify and Accelerate Modeling Impact. Machine learning is driving a revolution in risk, fraud, andOPTIMIZATION ENGINE
The core of our optimization solution lies in our optimization engine, an ensemble of global and Bayesian optimization algorithms that can effectively optimize any model, from long time-to-train deep learning models to cutting-edge trading strategies. SIGOPT API DOCUMENTATION SigOpt takes any research pipeline and tunes it, right in place, boosting your business objectives. Our cloud-based ensemble of optimization algorithms is proven and seamless to deploy. BAYESIAN OPTIMIZATION 101 The Basics of Bayesian Optimization. Here is a quick visual summary of how Bayesian optimization works. Step 1: Sample the parameter space. Initialize the process by sampling the hyperparameter space either randomly or low-discrepancy sequencing and getting these observations . Step 2: Build a surrogate model. A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
SIGOPTSOLUTIONWHY OPTIMIZEFOR ENTERPRISEFOR ACADEMIADOCSPRODUCT SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure. To get started: Instrument your model code to track runs and model artifacts. Automate sample-efficient hyperparameter optimization. UNCERTAINTY 2: BAYESIAN OPTIMIZATION WITH UNCERTAINTY UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty COMMON PROBLEMS IN HYPERPARAMETER OPTIMIZATION Hyperparameter optimization is a powerful tool for unlocking the maximum potential of your model, but only when it is correctly implemented. Here, we are going to share seven common problems we’ve seen while executing hyperparameter optimization. #1 Trusting theDefaults.
A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
BAYESIAN OPTIMIZATION FOR REINFORCEMENT LEARNING UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
KRIGING VARIANCE: IMPROVEMENT VS. KNOWLEDGE GRADIENT Let us also assume the variance of the observation is 0.5 across all options and that the best incumbent outcome is 9.5. Computing the KG and EI values using these numbers, we can compare the results in Figure 3. Figure 3. The KG and EI values for the five options (in THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORSAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } } SIGOPTSOLUTIONWHY OPTIMIZEFOR ENTERPRISEFOR ACADEMIADOCSPRODUCT SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure. To get started: Instrument your model code to track runs and model artifacts. Automate sample-efficient hyperparameter optimization. UNCERTAINTY 2: BAYESIAN OPTIMIZATION WITH UNCERTAINTY UNCERTAINTY 1: MODELING WITH UNCERTAINTY This is the first of three blog posts during which we explore the concept of uncertainty – or noise – and its implications for Bayesian optimization. This is part of our series of blog content on research that informs our product and methodologies. Uncertainty 1: Modeling with Uncertainty Uncertainty 2: Bayesian Optimization with Uncertainty Uncertainty COMMON PROBLEMS IN HYPERPARAMETER OPTIMIZATION Hyperparameter optimization is a powerful tool for unlocking the maximum potential of your model, but only when it is correctly implemented. Here, we are going to share seven common problems we’ve seen while executing hyperparameter optimization. #1 Trusting theDefaults.
A COMPARISON OF HYPERPARAMETER OPTIMIZATION METHODS Check us out at upcoming events. 562-7-SIGOPT. sales@sigopt.com. Contact Us. Blog / A Comparison of Hyperparameter Optimization Methods. When it’s time to ensure that your model, whether that’s a recommendation system, computer vision classifier, or trading strategy is performing as well as it possibly can, you’ll need tochoose the right
BAYESIAN OPTIMIZATION FOR REINFORCEMENT LEARNING UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
KRIGING VARIANCE: IMPROVEMENT VS. KNOWLEDGE GRADIENT Let us also assume the variance of the observation is 0.5 across all options and that the best incumbent outcome is 9.5. Computing the KG and EI values using these numbers, we can compare the results in Figure 3. Figure 3. The KG and EI values for the five options (in THE CASE OF THE MYSTERIOUS AWS ELB 504 ERRORSAPI ERRORS - SIGOPT
API Errors. Every request to the SigOpt API returns an HTTP status code. Status code 2XX indicates a successful call and the response will contain the requested resource. Other status codes indicate failure. The response bodies will have the following format: { "error": { "message": "No API token provided." } }PRODUCT | SIGOPT
Our optimization amplifies model performance and accelerates model development. SigOpt was founded to empower the world’s experts by building software solutions that accelerate and amplify the impact of their machine learning, deep learning, and simulation models.SOLUTION | SIGOPT
562-7-SIGOPT. sales@sigopt.com. Contact Us. Solution. Empower Your Experts. SigOpt is a model optimization and experimentation solution uniquely designed to augment your experts. Your researchers bring their expertise to bear on the data and features, whileRESOURCES | SIGOPT
Recurrent Neural Network. Modeling is messy. Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join SigOpt to get free access to these features today. For a limited time, we are offering free access to our complete product, including hyperparameter optimization. Sign up for free access here.CONTACT | SIGOPT
562-7-SIGOPT (562-774-4678) contact@sigopt.com • sales@sigopt.com. 100 Bush Street, Suite 1100, San Francisco , CA 94104. Get Directions. Modeling is messy. Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join SigOpt to get free access to these features today. For a limited time, we are offering EXPERIMENT MANAGEMENT 562-7-SIGOPT. sales@sigopt.com. Contact Us. Beta. Experiment Management. Track training runs with a few lines of code. Customize plots in an interactive dashboard. Automate hyperparameter optimization. Compatible with any library, stack, or codingenvironment.
SIGN UP - SIGOPT
Thanks for your interest in . SigOpt!. SigOpt supports model development with software to track model artifacts, visualize training and metric comparisons, automate hyperparameter optimization, and schedule jobs. Learn more about our product on our website.. By signing up, you get free access to: Experiments: Automate sample-efficient hyperparameter optimization SIGOPT: AMPLIFY YOUR RESEARCH SigOpt takes any research pipeline and tunes it, right in place, boosting your business objectives. Our cloud-based ensemble of optimization algorithms is proven and seamless to deploy. UNSUPERVISED LEARNING ALGORITHMS WITH BAYESIAN OPTIMIZATION Unsupervised learning algorithms can be a powerful tool for boosting the performance of your supervised models when labeling is an expensive or slow process. Tuning automatically brings each model to its full potential. SigOpt was built to help with this non-intuitivetask.
SIGOPT API DOCUMENTATION SigOpt takes any research pipeline and tunes it, right in place, boosting your business objectives. Our cloud-based ensemble of optimization algorithms is proven and seamless to deploy. HIGHLIGHT: AUTOMATING BAYESIAN OPTIMIZATION WITH Automated Bayesian optimization will place the next observation at the optimum (highest function value). In the figure above, we show two instances of Bayesian optimization where our goal is to maximize the (unknown-to-the-methods) red objective function. Both instances use expected improvement as acquisition function. This website stores cookies on your computer. These cookies are used to improve your website experience and provide more personalized services to you, both on this website and through other media. To find out more about the cookies we use, see our Privacy Policy. We won't track your information when you visit our site. But in order to comply with your preferences, we'll have to use just one tiny cookie so that you're not asked to make this choice again.Accept Decline
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BUILD THE BEST MODELS SigOpt is a standardized, scalable, enterprise-grade optimization platform and API designed to unlock the potential of your modeling pipelines. This fully agnostic software solution accelerates, amplifies, and scales the model development process. SIGOPT WAS FOUNDED TO EMPOWER THE WORLD’S EXPERTS Our black-box hyperparameter optimization solution automates model tuning to accelerate the model development process and amplify the impact of models in production at scale. This process empowers our customers to generate more high-performing models in production. And with more models in production, they earn a higher return on their modeling investment. Why Optimize? Why SigOpt? AUGMENT EXPERT PRODUCTIVITY “SigOpt offers an advanced and scalable solution capable of impacting the performance of any type of AI model. Whether working on simulations, reinforcement learning, deep neural networks, machine learning or anything in between, researchers can use SigOpt to track, analyze, and tune their models.”George Hoyem
Managing Partner
In-Q-Tel
Model-Analysis-Selection AMPLIFY IMPACT OF YOUR MODELS “We’ve integrated SigOpt’s optimization service and are now able to get better results faster and cheaper than any solution we’veseen before.”
Matt Adereth
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Production-and-Deployment ACCELERATE MODEL DEVELOPMENT “Integrating SigOpt into our modeling platform empowers our team to more efficiently experiment, optimize and, ultimately, model atscale.”
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OpenAI
GLOBALLY RECOGNIZED AND AWARD WINNING EMPOWERING LEADING AI FIRMS WORLDWIDE POWERING LEADING RESEARCH BACKED BY THE WORLD’S TOP INVESTORS PARTNERED WITH OTHER AI LEADERS GLOBALLY RECOGNIZED AND AWARD WINNING EMPOWERING LEADING AI FIRMS WORLDWIDE BUILT TO DELIVER ENTERPRISE RESULTS AT SCALESolutions
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Modular: Embed in any platform With just a few lines of code, your team can embed SigOpt in any workflow, regardless of the type of machine learning platform, model management solution, cloud infrastructure, client library, or model type that is being used. We are dedicated to designing solutions that meet your experts where they are, rather than forcing them to build a workflow that accommodates our products.*
Impactful: Augment your experts SigOpt drives modeling impact in two ways. First, we maximize the impact of your experts on any data science problem by augmenting rather than replacing them the way that other AutoML solutions do. Second, while acknowledging there is no free lunch, our solution reaches the global optima better, faster and cheaper than alternative methods for the full volume, variety and complexity of your models.*
Scalable: Optimize any model Ours is the only hyperparameter optimization solution that supports up to 100 hyperparameters or offers 100x parallelism so your team can optimize their high dimensional models more quickly than ever before. It also incorporates a variety of advanced optimization features such as multimetric optimization, multitask optimization and conditional parameters that are designed to empower your team to solve entirely new business problems with entirely new models. Modular: Embed in any platform With just a few lines of code, your team can embed SigOpt in any workflow, regardless of the type of machine learning platform, model management solution, cloud infrastructure, client library, or model type that is being used. We are dedicated to designing solutions that meet your experts where they are, rather than forcing them to build a workflow that accommodates our products. Impactful: Augment your experts SigOpt drives modeling impact in two ways. First, we maximize the impact of your experts on any data science problem by augmenting rather than replacing them the way that other AutoML solutions do. Second, while acknowledging there is no free lunch, our solution reaches the global optima better, faster and cheaper than alternative methods for the full volume, variety and complexity of your models. Scalable: Optimize any model Ours is the only hyperparameter optimization solution that supports up to 100 hyperparameters or offers 100x parallelism so your team can optimize their high dimensional models more quickly than ever before. It also incorporates a variety of advanced optimization features such as multimetric optimization, multitask optimization and conditional parameters that are designed to empower your team to solve entirely new business problems with entirely new models. Learn more about SigOpt for Enterprise BACKED BY LEADING APPLIED RESEARCH Applying optimization techniques to enterprise modeling use cases comes with its own host of unique challenges. Our research team is passionate about evolving our optimization solution to address these challenges so our customers can trust the performance of their models in production and at scale. In the process, our customers often abandon grid search, random search, and open source Bayesianoptimization.
Learn more about our research THE MOST ADVANCED OPTIMIZATION SOLUTION10-100X FASTER
Whether utilizing our leading Optimization Engine or advanced features like Multitask Optimization, our customers tune their models much faster than when using alternative methods. This becomes particularly important as teams increase the complexity or dimensionality of their models. Explore use cases in which SigOpt tunes models 10x faster thanother methods.
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SigOpt significantly increases computational efficiency with an ensemble of Bayesian and global optimization algorithms that are designed to efficiently explore and exploit any parameter space. When combined with leading AI hardware, this approach results in enormous cost savings that scale with modeling over time. Learn how AWS, NVIDIA and SigOpt efficiently scale model training and tuning.BETTER PERFORMANCE
There is no free lunch, but SigOpt consistently outperforms grid, random, and other Bayesian search methods across a wide cross-section of problems. Though the primary benefit of SigOpt is that it can efficiently optimize any model, it most often delivers better performance along the way. Learn how we compare in a stratified analysis of Bayesian optimization methods. Learn more about our productRESOURCE LIBRARY
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Modeling is messy.
Clean it up with a few lines of code. You can now track runs and visualize training in SigOpt. Join our beta to get free access to these features today. And for a limited time, we are offering free access to our complete product, including hyperparameter optimization. Sign up for the beta ×Details
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