Cloud-Native Monitoring for Cloud-Native Apps

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Rob Skillington, CTO (left), Martin Mao, CEO (right)

Today, we are thrilled to announce Lux is leading Chronosphere’s $43.4 million Series B round alongside existing investors Greylock and Lee Fixel, with participation from General Atlantic.

We were fortunate to be investors in Chronosphere’s Series A in 2019, seeing it as one of the rare opportunities to partner with a peerless founding team tackling a problem they alone are uniquely suited to solve. …


Accelerating innovation in life science R&D

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Biotech and biopharma have been on a path of rapidly accelerating innovation. Over the past decade, we have seen the advent of new types of gene therapies, the adaptation of CRISPR, and the rise of computational approaches to drug discovery. And yet with these new technologies comes increasingly complex challenges. Biologics, which are orders of magnitude more complex in structure than small molecules, now make up ~50% of the current global drug pipeline — and that number is only growing. However, as the level of this complexity increased, the tooling did not. …


A data platform built by ex-Uber engineers to operationalize machine learning

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Tecton cofounders: Kevin Stumpf, Mike Del Balso, and Jeremy Hermann

Much of the past two decades of innovation and evolution in data infrastructure have been born out of the largest tech companies. Google and Yahoo were credited for the Hadoop platforms — Facebook built Cassandra and Presto to store and query data at large volumes — Kafka was created inside LinkedIn — and Uber quickly scaled and operationalized machine learning across the company.

For many enterprises, running machine learning in production has been out of the realm of possibility. Talent is scarce, the state-of-the-art is evolving rapidly, and there is a lack of infrastructure readily available to operationalize models. While some tech companies have been running machine learning in production for years, there exists a disconnect between the select few that wield such capabilities and much of the rest of the Global 2000. Some internal ML platforms at these tech companies have become well known, such as Google’s TFX, Facebook’s FBLearner, and Uber’s Michelangelo. What many of these companies learned through their own experiences of deploying machine learning is that much of the complexity resides not in the selection and training of models, but rather in managing the data-focused workflows (feature engineering, serving, monitoring, etc.) …


Fostering the largest open source community in Natural Language Processing

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Term sheet signing with Josh (Lux Capital) on the left, Clement (Hugging Face CEO) in the middle, and Julien (Hugging Face CTO) on the right

A seminal moment in machine learning took place on Sept 30, 2012 when a convolutional neural network called AlexNet achieved groundbreaking results in the ImageNet competition. This kicked off a race of rapidly improving computer vision models to the point where the technology outperformed humans in many tasks. These breakthroughs accelerated industries such as autonomous vehicles, consumer mobile applications, and created new multi billion dollar opportunities around computing architectures for machine learning training and inference.

Natural Language Processing (NLP), another discipline of machine learning has seemed to lag behind in progress relative to computer vision. Recently NLP may have had its “ImageNet” moment due to new transformers models (e.g., GPT2 and BERT) shattering performance benchmarks. Weeks ago, Google announced their biggest update in years to search with the implementation of BERT neural networks to improve results. The market for bringing NLP to enterprises and consumers is incredibly exciting — very few professions deal with images (and consequently computer vision) but nearly all jobs work with text and language (and thus NLP). …


A pharmacy for digital and connected devices

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Today we are announcing our investment in Elektra Labs — a healthcare-security company building a platform to review and dispense connected technologies remotely. More recently software has been finding its way into many aspects of the healthcare system, with examples such as EMR deployments from Epic, tracking biomarkers with wearables in clinical trials, and digital therapeutics. Most of the software today was created to service big pharma and hospitals but very little has been purpose built for digital medicine.

Last year, Lux was first institutional investor to join Andy and Elektra Labs, and now we are excited to invest in her $2.9M seed round with great coinvestors such as Maverick, Founders Collective, Arkitekt Ventures, Boost VC, SV Angel and an all star group of angels. Many people say venture capital is business about finding incredible individuals that can rewire industries. I’ve known Andy for years and as soon as I learned about her vision for Elektra Labs, I wanted Lux to be part of the journey. We invested behind the belief that managing connected devices in healthcare is a growing and underserved problem. We have seen the zeitgeist of privacy when it comes to applications on mobile phones, and now just starting to see the spotlight shine on our most sensitive information — personal healthcare data. …


Ex-Uber engineers push forward the frontier of time series infrastructure

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At Lux Capital we believe there are directional trends of technological progress. One clear trend is that many enterprises are moving from batch processing to real time streaming, monitoring a few aspects of a business to monitoring many aspects, and operating applications in a few regions to operating in many regions.

We also believe in each wave of innovation there are a few soothsayers that see the problems first and out of necessity invent solutions. For the tech giants, many of these challenges were around computing infrastructure. Google and Yahoo were the first companies that required distributed data processing which led to the Hadoop platforms — Facebook was the first network that experienced social at scale which spawned Cassandra, React Native, and GraphQL — and Uber was one of the first companies confronted with infrastructure challenges as fast moving product teams leveraged time-series data and high cardinality metrics. …


Today, the pervasiveness of AI is still fundamentally limited in robotics because every company is collecting their own fragmented (and often incomplete) data set — if any at all. Moreover, the large industrial robotic arm vendors do not easily provide users access to low level data such as currents, torques, etc., to properly train their industrial arms. Because of fragmentation and lack of raw data, it is nearly impossible to train robots to be intelligent and introduce new applications. When there is a common platform shared across all robotic manufacturers, then finally we can unleash the power of reinforcement learning and let our imaginations run wild. …

About

Brandon Reeves

VC @Lux_capital

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