Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not \textit{model-aware}, rather they are designed to apply to a \textit{generic} model; this leaves significant optimization performance on the table. Second, using these systems requires \textit{domain knowledge} such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently -- and in a more seamless and user-friendly way -- than existing black-box hyperparameter tuning systems.

Prior to joining SigOpt, he was working on the customer side of things, where he was bringing SigOpt into production for one of our big enterprise customers. Tobias has extensive experience creating enterprise Machine Learning pipelines; starting at the earliest stages with automated data annotation, spinning up infrastructure and optimizing towards a specific business objective. Tobias comes out of Denmark with a background in applied mathematics and decided to move to California both for work, but also to be able to climb and surf in some of the world's best settings.

In this blog post we highlight some of the key takeaways from David Austin's presentation on how to supercharge a 1st place Kaggle solution to higher performance. David Austin is a Senior Principal Artificial Intelligence Engineer at Intel working on industrial applications within the Internet of Things space. In his spare time, he spends, in his own words, way too much time participating in Kaggle competitions and has since 2018 held the title of grandmaster. In the presentation David Austin walks though the Iceberg Classifier Challenge, where the participants are asked to classify radar images into either icebergs or ships to improve safety at sea. At the time of the Iceberg Classifier Challenge it was the computer vision challenge with the most participants ever on Kaggle.

It's hoped that the combination of SigOpt's AI scaling software and Intel's hardware will give Intel competitive advantages in emerging tech. San Francisco's SigOpt is being acquired by Intel for an undisclosed sum. SigOpt's platform enables the optimisation of artificial intelligence (AI) software models at scale. Its present customer base includes Fortune 500 companies across different industries, as well as leading research institutions, universities and consortiums. With this acquisition, Intel plans to use SigOpt's software across its own AI hardware products to accelerate and grow its AI offerings to developers.

Intel has confirmed that it is buying SigOpt Inc., an artificial intelligence startup developing software platforms to optimize AI models.

Intel has been doubling down on building chips and related architecture for the next generation of computing, and today it announced an acquisition that will bolster its expertise and work specifically in one area of future technology: artificial intelligence. The semiconductor giant today announced that it has acquired SigOpt, a startup out of San Francisco that has built an optimization platform that can be used to run modeling and simulations (two key applications of AI tech) in a better way. Anthony described SigOpt as a startup built to "optimize everything" when we covered its Series A, but Intel specifically will be integrating the tech into its AI business, specifically into its AI Analytics Toolkit, a spokesperson tells me. Terms of the deal were not disclosed, but SigOpt already counted a number of large enterprises -- "SigOpt's customer base includes Fortune 500 companies across industries, as well as leading research institutions, universities and consortiums using its products" -- among its customers. The product was still in a closed beta, however.

What's New: Today, Intel announced it will acquire SigOpt, a San Francisco-based provider of a leading platform for the optimization of artificial intelligence (AI) software models at scale. SigOpt's AI software technologies deliver productivity and performance gains across hardware and software parameters, use cases and workloads in deep learning, machine learning and data analytics. Intel plans to use SigOpt's software technologies across Intel's AI hardware products to help accelerate, amplify and scale Intel's AI software solution offerings to developers. "In the new intelligence era, AI is driving the compute needs of the future. It is even more important for software to automatically extract the best compute performance while scaling AI models. SigOpt's AI software platform and data science talent will augment Intel software, architecture, product offerings and teams, and provide us with valuable customer insights. We welcome the SigOpt team and its customers to the Intel family."

With the aim to scale artificial intelligence (AI) productivity, chipmaker Intel said that it is acquiring San Francisco-based firm SigOpt that deals in the optimization of AI software models at scale. The deal is expected to close this quarter. Transaction terms were not disclosed. SigOpt's team – including SigOpt CEO and co-founder Scott Clark and CTO and co-founder Patrick Hayes – will join the Machine Learning Performance team in Intel Architecture, Graphics and Software (IAGS). Raja Koduri, Intel senior vice president, chief architect and general manager of IAGS said: "SigOpt's AI software platform and data science talent will augment Intel software, architecture, product offerings and teams, and provide us with valuable customer insights."

As I wrote in March, Google sees its cloud services as fueling the "democratization of AI" by abstracting many of the hard implementation details of building and using an AI software stack into cloud services. The early examples, such as Amazon AI, Azure Cognitive Services and Google Cloud Machine Learning Services are splendid examples of encapsulating sophisticated AI functions in a fairly straightforward service with an API wrapper. However, the high-level services mostly focus on the well-trodden paths of image and speech recognition; domains that have long catalyzed AI research. While such applications certainly have many uses in business, including for conversational interfaces as I detail here, they don't address the vast majority of business problems that could benefit from machine learning optimization and where applying AI still requires too much time and expertise. As the ARCHITECT blog rightly points out, AI research has often focused on games like Chess and Go, or handy add-ons to online consumer services like automatic image tagging and voice commands, not hard business problems.

Machine learning is already pervasive: Most people probably don't realize it. "Whether or not you know it, odds are that machine learning powers applications that you use every day," says Bill Brock, VP of engineering at Very. "Machine learning has revolutionized countless industries; it's the underlying technology for many apps in your smartphone, from virtual assistants like Siri to predicting traffic patterns with Google Maps." Perhaps you care more about the accuracy of that traffic prediction or the voice assistant's response than what's under the hood – and understandably so. But as machine learning use cases continue to increase, you will find yourself needing to explain at least the basics of the technology to folks outside of IT, whether it's to get buy-in, to showcase the work of your team, or simply to build better communication and understanding between departments. Your understanding of ML could also bolster the long-term results of your artificial intelligence strategy.