machine learning
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Edge computing startup Edgify secures $6.5M seed from Octopus, Mangrove and semiconductor
Edgify, which builds AI for edge computing, has secured a $6.5 million seed funding round backed by Octopus Ventures, Mangrove Capital Partners and an unnamed semiconductor giant. The name was not released but TechCrunch understands it may be Intel Corp. or Qualcomm Inc.
Edgify’s technology allows “edge devices” (devices at the edge of the internet) to interpret vast amounts of data, train an AI model locally and then share that learning across its network of similar devices. This then trains all the other devices in anything from computer vision, NLP, voice recognition or any other form of AI.
The technology can be applied to anything from MRI machines, connected cars, checkout lanes, mobile devices and anything that has a CPU, GPU or NPU. Edgify’s technology is already being used in supermarkets, for instance.
Ofri Ben-Porat, CEO and co-founder of Edgify, commented in a statement: “Edgify allows companies, from any industry, to train complete deep learning and machine learning models, directly on their own edge devices. This mitigates the need for any data transfer to the Cloud and also grants them close to perfect accuracy every time, and without the need to retrain centrally.”
Mangrove partner Hans-Jürgen Schmitz, who will join Edgify’s Board comments: “We expect a surge in AI adoption across multiple industries with significant long-term potential for Edgify in medical and manufacturing, just to name a few.”
Simon King, partner and Deep Tech Investor at Octopus Ventures added: “As the interconnected world we live in produces more and more data, AI at the edge is becoming increasingly important to process large volumes of information.”
So-called “edge computing” is seen as being one of the forefronts of deep tech right now.
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Nym Health raises $16.5 million for its auditable machine learning tools for automating hospital billing
A little less than two years after raising its seed round, the Israeli-based Nym Health has added another $16.5 million to its cash haul so it can roll out its technology developing auditable machine learning tools for automating hospital billing.
The new financing came from investors, including GV (the investment arm of Google previously known as Google Ventures), and will be used by the company to expand its technology development and sales and marketing efforts across the U.S.
Billing has been a huge problem for healthcare systems in the U.S., thanks to complicated coding that needs to be entered to ensure insurance providers pay for the services medical professionals give to patients.
Nym claims to have solved the problem by developing technologies that can convert medical charts and electronic medical records from physician’s consultations into proper billing codes automatically. The company uses natural language processing and taxonomies that were specifically developed to understand clinical language to determine the optimal charge for each procedure, examination and diagnostic conducted for a patient, according to Nym.
The company was founded in 2018 by two former members of Israel’s 8200 cybersecurity unit of the army. Adam Rimon and Amihai Neiderman both wanted to work on something together and Neiderman was set on doing something in the medical space involving natural language processing. Rimon had just finished a doctorate in computational linguistics, so the move into charting and medical coding seemed natural.
“Because of our approach we can generate full audit trails,” said Neiderman. “We can explain how we understood everything in patient charts.”
Having automated processes that are also auditable is important for healthcare providers in case they need to provide justification to insurance companies for the services they performed.
Nym’s software can’t address fraud if physicians are padding their bills with services they didn’t offer, but it can provide an audit and justification for the services that a hospital coded for — and potentially wring more money for hospitals that lose out thanks to improperly coded bills. “On the medical decision-making we never intervene. We assume that the physician is trying to do their best and they’re sticking to the protocol,” said Neiderman.
Interest in developing better billing systems for healthcare is high among venture investors, considering that coding related denials of payment can cost hospitals $15 billion, according to Nym. It’s a service that brought attention not just from GV, but Bessemer Venture Partners, Dynamic Loop Capital, Lightspeed, Tiger Global, and angel investors including Zach Weinberg and Nat Turner from Flatiron Health.
“Inaccurate coding is bad for everybody,” says Ben Robbins, a venture partner at GV.
Nym charges between $1 and $4 per chart it analyzes, and is already working with around 40 medical providers in the U.S., according to the company.
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Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production
Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers work more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute.
Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.
The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.
“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”
As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.
What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”
The service is now in private beta.
With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.
“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”
“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”
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Strike Graph raises $3.9M to help automate security audits
Compliance automation isn’t exactly the most exciting topic, but security audits are big business and companies that aim to get a SOC 2, ISO 207001 or FedRamp certification can often spend six figures to get through the process with the help of an auditing service. Seattle-based Strike Graph, which is launching today and announcing a $3.9 million seed funding round, wants to automate as much of this process as possible.
The company’s funding round was led by Madrona Venture Group, with participation from Amplify.LA, Revolution’s Rise of the Rest Seed Fund and Green D Ventures.
Strike Graph co-founder and CEO Justin Beals tells me that the idea for the company came to him during his time as CTO at machine learning startup Koru (which had a bit of an odd exit last year). To get enterprise adoption for that service, the company had to get a SOC 2 security certification. “It was a real challenge, especially for a small company. In talking to my colleagues, I just recognized how much of a challenge it was across the board. And so when it was time for the next startup, I was just really curious,” he told me.
Image Credits: Strike Graph
Together with his co-founder Brian Bero, he incubated the idea at Madrona Venture Labs, where he spent some time as Entrepreneur in Residence after Koru.
Beals argues that today’s process tends to be slow, inefficient and expensive. The idea behind Strike Graph, unsurprisingly, is to remove as many of these inefficiencies as is currently possible. The company itself, it is worth noting, doesn’t provide the actual audit service. Businesses will still need to hire an auditing service for that. But Beals also argues that the bulk of what companies are paying for today is pre-audit preparation.
“We do all that preparation work and preparing you and then, after your first audit, you have to go and renew every year. So there’s an important maintenance of that information.”
Image Credits: Strike Graph
When customers come to Strike Graph, they fill out a risk assessment. The company takes that and can then provide them with controls for how to improve their security posture — both to pass the audit and to secure their data. Beals also noted that soon, Strike Graph will be able to help businesses automate the collection of evidence for the audit (say your encryption settings) and can pull that in regularly. Certifications like SOC 2, after all, require companies to have ongoing security practices in place and get re-audited every 12 months. Automated evidence collection will launch in early 2021, once the team has built out the first set of its integrations to collect that data.
That’s also where the company, which mostly targets mid-size businesses, plans to spend a lot of its new funding. In addition, the company plans to focus on its marketing efforts, mostly around content marketing and educating its potential customers.
“Every company, big or small, that sells a software solution must address a broad set of compliance requirements in regards to security and privacy. Obtaining the certifications can be a burdensome, opaque and expensive process. Strike Graph is applying intelligent technology to this problem — they help the company identify the appropriate risks, enable the audit to run smoothly and then automate the compliance and testing going forward,” said Hope Cochran, managing director at Madrona Venture Group. “These audits were a necessary pain when I was a CFO, and Strike Graph’s elegant solution brings together teams across the company to move the business forward faster.”
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As it closes in on Arm, Nvidia announces UK supercomputer dedicated to medical research
As Nvidia continues to work through its deal to acquire Arm from SoftBank for $40 billion, the computing giant is making another big move to lay out its commitment to investing in U.K. technology. Today the company announced plans to develop Cambridge-1, a new £40 million AI supercomputer that will be used for research in the health industry in the country, the first supercomputer built by Nvidia specifically for external research access, it said.
Nvidia said it is already working with GSK, AstraZeneca, London hospitals Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore to use the Cambridge-1. The supercomputer is due to come online by the end of the year and will be the company’s second supercomputer in the country. The first is already in development at the company’s AI Center of Excellence in Cambridge, and the plan is to add more supercomputers over time.
The growing role of AI has underscored an interesting crossroads in medical research. On one hand, leading researchers all acknowledge the role it will be playing in their work. On the other, none of them (nor their institutions) have the resources to meet that demand on their own. That’s driving them all to get involved much more deeply with big tech companies like Google, Microsoft and, in this case, Nvidia, to carry out work.
Alongside the supercomputer news, Nvidia is making a second announcement in the area of healthcare in the U.K.: it has inked a partnership with GSK, which has established an AI hub in London, to build AI-based computational processes that will be used in drug vaccine and discovery — an especially timely piece of news, given that we are in a global health pandemic and all drug makers and researchers are on the hunt to understand more about, and build vaccines for, COVID-19.
The news is coinciding with Nvidia’s industry event, the GPU Technology Conference.
“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Jensen Huang, founder and CEO of Nvidia, in his keynote at the event. “The Cambridge-1 supercomputer will serve as a hub of innovation for the U.K., and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”
The company plans to dedicate Cambridge-1 resources in four areas, it said: industry research, in particular joint research on projects that exceed the resources of any single institution; university granted compute time; health-focused AI startups; and education for future AI practitioners. It’s already building specific applications in areas, like the drug discovery work it’s doing with GSK, that will be run on the machine.
The Cambridge-1 will be built on Nvidia’s DGX SuperPOD system, which can process 400 petaflops of AI performance and 8 petaflops of Linpack performance. Nvidia said this will rank it as the 29th fastest supercomputer in the world.
“Number 29” doesn’t sound very groundbreaking, but there are other reasons why the announcement is significant.
For starters, it underscores how the supercomputing market — while still not a mass-market enterprise — is increasingly developing more focus around specific areas of research and industries. In this case, it underscores how health research has become more complex, and how applications of artificial intelligence have both spurred that complexity but, in the case of building stronger computing power, also provides a better route — some might say one of the only viable routes in the most complex of cases — to medical breakthroughs and discoveries.
It’s also notable that the effort is being forged in the U.K. Nvidia’s deal to buy Arm has seen some resistance in the market — with one group leading a campaign to stop the sale and take Arm independent — but this latest announcement underscores that the company is already involved pretty deeply in the U.K. market, bolstering Nvidia’s case to double down even further. (Yes, chip reference designs and building supercomputers are different enterprises, but the argument for Nvidia is one of commitment and presence.)
“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise,” said Dr. Hal Barron, chief scientific officer and president, R&D, GSK, in a statement. “NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients. Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the U.K.’s outstanding scientists.”
“The use of big data, supercomputing and artificial intelligence have the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” added James Weatherall, PhD, head of Data Science and AI, AstraZeneca, in his statement.
“Recent advances in AI have seen increasingly powerful models being used for complex tasks such as image recognition and natural language understanding,” said Sebastien Ourselin, head, School of Biomedical Engineering & Imaging Sciences at King’s College London. “These models have achieved previously unimaginable performance by using an unprecedented scale of computational power, amassing millions of GPU hours per model. Through this partnership, for the first time, such a scale of computational power will be available to healthcare research – it will be truly transformational for patient health and treatment pathways.”
Dr. Ian Abbs, chief executive & chief medical director of Guy’s and St Thomas’ NHS Foundation Trust Officer, said: “If AI is to be deployed at scale for patient care, then accuracy, robustness and safety are of paramount importance. We need to ensure AI researchers have access to the largest and most comprehensive datasets that the NHS has to offer, our clinical expertise, and the required computational infrastructure to make sense of the data. This approach is not only necessary, but also the only ethical way to deliver AI in healthcare – more advanced AI means better care for our patients.”
“Compact AI has enabled real-time sequencing in the palm of your hand, and AI supercomputers are enabling new scientific discoveries in large-scale genomic data sets,” added Gordon Sanghera, CEO, Oxford Nanopore Technologies. “These complementary innovations in data analysis support a wealth of impactful science in the U.K., and critically, support our goal of bringing genomic analysis to anyone, anywhere.”
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Pixie Labs raises $9.15M Series A round for its Kubernetes observability platform
Pixie, a startup that provides developers with tools to get observability into their Kubernetes-native applications, today announced that it has raised a $9.15 million Series A round led by Benchmark, with participation from GV. In addition, the company also today said that its service is now available as a public beta.
The company was co-founded by Zain Asgar (CEO), a former Google engineer working on Google AI and adjunct professor at Stanford, and Ishan Mukherjee (CPO), who led Apple’s Siri Knowledge Graph product team and also previously worked on Amazon’s Robotics efforts. Asgar had originally joined Benchmark to work on developer tools for machine learning. Over time, the idea changed to using machine learning to power tools to help developers manage large-scale deployments instead.
“We saw data systems, this move to the edge, and we felt like this old cloud 1.0 model of manually collecting data and shipping it to databases in the cloud seems pretty inefficient,” Mukherjee explained. “And the other part was: I was on call. I got gray hair and all that stuff. We felt like we could build this new generation of developer tools and get to Michael Jordan’s vision of intelligent augmentation, which is giving creatives tools where they can be a lot more productive.”
Image Credits: Pixie
The team argues that most competing monitoring and observability systems focus on operators and IT teams — and often involve a long manual setup process. But Pixie wants to automate most of this manual process and build a tool that developers want to use.
Pixie runs inside a developer’s Kubernetes platform and developers get instant and automatic visibility into their production environments. With Pixie, which the team is making available as a freemium SaaS product, there is no instrumentation to install. Instead, the team uses relatively new Linux kernel techniques like eBPF to collect data right at the source.
“One of the really cool things about this is that we can deploy Pixie in about a minute and you’ll instantly get data,” said Asgar. “Our goal here is that this really helps you when there are cases where you don’t want your business logic to be full of monitoring code, especially if you forget something — when you have an outage.”
Image Credits: Pixie
At the core of the developer experience is what the company calls “Pixie scripts.” Using a Python-like language (PxL), developers can codify their debugging workflows. The company’s system already features a number of scripts written by the team itself and the community at large. But as Asgar noted, not every user will write scripts. “The way scripts work, it’s supposed to capture human knowledge in that problem. We don’t expect the average user — or even the way-above-average developer — ever to touch a script or write one. They’re just going to use it in a specific scenario,” he explained.
Looking ahead, the team plans to make these scripts and the scripting language more robust and usable to allow developers to go from passively monitoring their systems to building scripts that can actively take actions on their clusters based on the monitoring data the system collects.
“Zain and Ishan’s provocative idea was to move software monitoring to the source,” said Eric Vishria, general partner at Benchmark. “Pixie enables engineering teams to fundamentally rethink their monitoring strategy as it presents a vision of the future where we detect anomalous behavior and make operational decisions inside the infrastructure layer itself. This allows companies of all sizes to monitor their digital experiences in a more responsive, cost-effective and scalable manner.”
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Datasaur snags $3.9M investment to build intelligent machine learning labeling platform
As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it’s working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams.
The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman.
Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and, most recently, at Apple. Regardless of the company, he consistently saw a problem around organizing machine learning labeling teams, one that he felt he was uniquely situated to solve because of his experience.
“I have spent millions of dollars [in budget over the years] and spent countless hours gathering labeled data for my engineers. I came to recognize that this was something that was a problem across all the companies that I’ve been at. And they were just consistently reinventing the wheel and the process. So instead of reinventing that for the third time at Apple, my most recent company, I decided to solve it once and for all for the industry. And that’s why we started Datasaur last year,” Lee told TechCrunch.
He built a platform to speed up human data labeling with a dose of AI, while keeping humans involved. The platform consists of three parts: a labeling interface; the intelligence component, which can recognize basic things so the labeler isn’t identifying the same thing over and over; and finally a team organizing component.
He says the area is hot, but to this point has mostly involved labeling consulting solutions, which farm out labeling to contractors. He points to the sale of Figure Eight in March 2019 and to Scale, which snagged $100 million last year as examples of other startups trying to solve this problem in this way, but he believes his company is doing something different by building a fully software-based solution.
The company currently offers a cloud and on-prem solution, depending on the customer’s requirements. It has 10 employees, with plans to hire in the next year, although he didn’t share an exact number. As he does that, he says he has been working with a partner at investor Initialized on creating a positive and inclusive culture inside the organization, and that includes conversations about hiring a diverse workforce as he builds the company.
“I feel like this is just standard CEO speak, but that is something that we absolutely value in our top of funnel for the hiring process,” he said.
As Lee builds out his platform, he has also worried about built-in bias in AI systems and the detrimental impact that could have on society. He says that he has spoken to clients about the role of labeling in bias and ways of combatting that.
“When I speak with our clients, I talk to them about the potential for bias from their labelers and built into our product itself is the ability to assign multiple people to the same project. And I explain to my clients that this can be more costly, but from personal experience I know that it can improve results dramatically to get multiple perspectives on the exact same data,” he said.
Lee believes humans will continue to be involved in the labeling process in some way, even as parts of the process become more automated. “The very nature of our existence [as a company] will always require humans in the loop, […] and moving forward I do think it’s really important that as we get into more and more of the long tail use cases of AI, we will need humans to continue to educate and inform AI, and that’s going to be a critical part of how this technology develops.”
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Privacy data management innovations reduce risk, create new revenue channels
Mark Settle
Contributor
Contributor
Mark Settle is a seven-time CIO, three-time CIO 100 award winner and two-time book author. His most recent book is “Truth from the Valley: A Practical Primer on IT Management for the Next Decade.”
Tomer Y. Avni
Contributor
Contributor
Tomer Y. Avni is an MBA/MS student at the Harvard Business School and the Harvard School of Engineering and Applied Sciences.
Privacy data mismanagement is a lurking liability within every commercial enterprise. The very definition of privacy data is evolving over time and has been broadened to include information concerning an individual’s health, wealth, college grades, geolocation and web surfing behaviors. Regulations are proliferating at state, national and international levels that seek to define privacy data and establish controls governing its maintenance and use.
Existing regulations are relatively new and are being translated into operational business practices through a series of judicial challenges that are currently in progress, adding to the confusion regarding proper data handling procedures. In this confusing and sometimes chaotic environment, the privacy risks faced by almost every corporation are frequently ambiguous, constantly changing and continually expanding.
Conventional information security (infosec) tools are designed to prevent the inadvertent loss or intentional theft of sensitive information. They are not sufficient to prevent the mismanagement of privacy data. Privacy safeguards not only need to prevent loss or theft but they must also prevent the inappropriate exposure or unauthorized usage of such data, even when no loss or breach has occurred. A new generation of infosec tools is needed to address the unique risks associated with the management of privacy data.
The first wave of innovation
A variety of privacy-focused security tools emerged over the past few years, triggered in part by the introduction of GDPR (General Data Protection Regulation) within the European Union in 2018. New capabilities introduced by this first wave of innovation were focused in the following three areas:
Data discovery, classification and cataloging. Modern enterprises collect a wide variety of personal information from customers, business partners and employees at different times for different purposes with different IT systems. This data is frequently disseminated throughout a company’s application portfolio via APIs, collaboration tools, automation bots and wholesale replication. Maintaining an accurate catalog of the location of such data is a major challenge and a perpetual activity. BigID, DataGuise and Integris Software have gained prominence as popular solutions for data discovery. Collibra and Alation are leaders in providing complementary capabilities for data cataloging.
Consent management. Individuals are commonly presented with privacy statements describing the intended use and safeguards that will be employed in handling the personal data they supply to corporations. They consent to these statements — either explicitly or implicitly — at the time such data is initially collected. Osano, Transcend.io and DataGrail.io specialize in the management of consent agreements and the enforcement of their terms. These tools enable individuals to exercise their consensual data rights, such as the right to view, edit or delete personal information they’ve provided in the past.
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Daily Crunch: Amazon unveils its own game-streaming platform
Amazon announces a new game service and plenty of hardware upgrades, tech companies team up against app stores and United Airlines tests a program for rapid COVID-19 testing. This is your Daily Crunch for September 24, 2020.
The big story: Amazon unveils its own game-streaming platform
Amazon’s competitor to Google Stadia and Microsoft xCloud is called Luna, and it’s available starting today at an early access price of $5.99 per month. Subscribers will be able to play games across PC, Mac and iOS, with more than 50 games in the library.
The company made the announcement at a virtual press event, where it also revealed a redesigned Echo line (with spherical speakers and swiveling screens), the latest Ring security camera and a new, lower-cost Fire TV Stick Lite.
You can also check out our full roundup of Amazon’s announcements.
The tech giants
App makers band together to fight for App Store changes with new ‘Coalition for App Fairness’ — Thirteen app publishers, including Epic Games, Deezer, Basecamp, Tile, Spotify and others, launched a coalition formalizing their efforts to force app store providers to change their policies or face regulation.
LinkedIn launches Stories, plus Zoom, BlueJeans and Teams video integrations as part of wider redesign — LinkedIn has built its business around recruitment, so this redesign pushes engagement in other ways as it waits for the job economy to pick up.
Facebook gives more details about its efforts against hate speech before Myanmar’s general election — This includes adding Burmese language warning screens to flag information rated false by third-party fact-checkers.
Startups, funding and venture capital
Why isn’t Robinhood a verb yet? — The latest episode of Equity discusses a giant funding round for Robinhood.
Twitter-backed Indian social network ShareChat raises $40 million — Following TikTok’s ban in India, scores of startups have launched short-video apps, but ShareChat has clearly established dominance.
Spotify CEO Daniel Ek pledges $1Bn of his wealth to back deeptech startups from Europe — Ek pointed to machine learning, biotechnology, materials sciences and energy as the sectors he’d like to invest in.
Advice and analysis from Extra Crunch
3 founders on why they pursued alternative startup ownership structures — At Disrupt, we heard about alternative approaches to ensuring that VCs and early founders aren’t the only ones who benefit from startup success.
Coinbase UX teardown: 5 fails and how to fix them — Many of these lessons, including the need to avoid the “Get Started” trap, can be applied to other digital products.
As tech stocks dip, is insurtech startup Root targeting an IPO? — Alex Wilhelm writes that Root’s debut could clarify Lemonade’s IPO and valuation.
(Reminder: Extra Crunch is our subscription membership program, which aims to democratize information about startups. You can sign up here.)
Everything else
United Airlines is making COVID-19 tests available to passengers, powered in part by Color — United is embarking on a new pilot project to see if easy access to COVID-19 testing immediately prior to a flight can help ease freedom of mobility.
Announcing the final agenda for TC Sessions: Mobility 2020 — TechCrunch reporters and editors will interview some of the top leaders in transportation.
The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.
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WhyLabs brings more transparancy to ML ops
WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.
The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.
Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.
“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”
Image Credits: WhyLabs
But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”
The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.
For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.
Image Credits: WhyLabs
According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.
“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”
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