machine learning
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Tecton, the company that pioneered the notion of the machine learning feature store, has teamed up with the founder of the open source feature store project called Feast. Today the company announced the release of version 0.10 of the open source tool.
The feature store is a concept that the Tecton founders came up with when they were engineers at Uber. Shortly thereafter an engineer named Willem Pienaar read the founder’s Uber blog posts on building a feature store and went to work building Feast as an open source version of the concept.
“The idea of Tecton [involved bringing] feature stores to the industry, so we build basically the best in class, enterprise feature store. […] Feast is something that Willem created, which I think was inspired by some of the early designs that we published at Uber. And he built Feast and it evolved as kind of like the standard for open source feature stores, and it’s now part of the Linux Foundation,” Tecton co-founder and CEO Mike Del Balso explained.
Tecton later hired Pienaar, who is today an engineer at the company where he leads their open source team. While the company did not originally start off with a plan to build an open source product, the two products are closely aligned, and it made sense to bring Pienaar on board.
“The products are very similar in a lot of ways. So I think there’s a similarity there that makes this somewhat symbiotic, and there is no explicit convergence necessary. The Tecton product is a superset of what Feast has. So it’s an enterprise version with a lot more advanced functionality, but at Feast we have a battle-tested feature store that’s open source,” Pienaar said.
As we wrote in a December 2020 story on the company’s $35 million Series B, it describes a feature store as “an end-to-end machine learning management system that includes the pipelines to transform the data into what are called feature values, then it stores and manages all of that feature data and finally it serves a consistent set of data.”
Del Balso says that from a business perspective, contributing to the open source feature store exposes his company to a different group of users, and the commercial and open source products can feed off one another as they build the two products.
“What we really like, and what we feel is very powerful here, is that we’re deeply in the Feast community and get to learn from all of the interesting use cases […] to improve the Tecton product. And similarly, we can use the feedback that we’re hearing from our enterprise customers to improve the open source project. That’s the kind of cross learning, and ideally that feedback loop involved there,” he said.
The plan is for Tecton to continue being a primary contributor with a team inside Tecton dedicated to working on Feast. Today, the company is releasing version 0.10 of the project.
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As companies create machine learning models, the operations team needs to ensure the data used for the model is of sufficient quality, a process that can be time-consuming. Bigeye (formerly Toro), an early-stage startup is helping by automating data quality.
Today the company announced a $17 million Series A led Sequoia Capital with participation from existing investor Costanoa Ventures. That brings the total raised to $21 million with the $4 million seed, the startup raised last May.
When we spoke to Bigeye CEO and co-founder Kyle Kirwan last May, he said the seed round was going to be focused on hiring a team — they are 11 now — and building more automation into the product, and he says they have achieved that goal.
“The product can now automatically tell users what data quality metrics they should collect from their data, so they can point us at a table in Snowflake or Amazon Redshift or whatever and we can analyze that table and recommend the metrics that they should collect from it to monitor the data quality — and we also automated the alerting,” Kirwan explained.
He says that the company is focusing on data operations issues when it comes to inputs to the model, such as the table isn’t updating when it’s supposed to, it’s missing rows or there are duplicate entries. They can automate alerts to those kinds of issues and speed up the process of getting model data ready for training and production.
Bogomil Balkansky, the partner at Sequoia who is leading today’s investment, sees the company attacking an important part of the machine learning pipeline. “Having spearheaded the data quality team at Uber, Kyle and Egor have a clear vision to provide always-on insight into the quality of data to all businesses,” Balkansky said in a statement.
As the founding team begins building the company, Kirwan says that building a diverse team is a key goal for them and something they are keenly aware of.
“It’s easy to hire a lot of other people that fit a certain mold, and we want to be really careful that we’re doing the extra work to [understand that just because] it’s easy to source people within our network, we need to push and make sure that we’re hiring a team that has different backgrounds and different viewpoints and different types of people on it because that’s how we’re going to build the strongest team,” he said.
Bigeye offers on-prem and SaaS solutions, and while it’s working with paying customers like Instacart, Crux Informatics and Lambda School, the product won’t be generally available until later in the year.
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School closures due to the pandemic have interrupted the learning processes of millions of kids, and without individual attention from teachers, reading skills in particular are taking a hit. Amira Learning aims to address this with an app that reads along with students, intelligently correcting errors in real time. Promising pilots and research mean the company is poised to go big as education changes, and it has raised $11 million to scale up with a new app and growing customer base.
In classrooms, a common exercise is to have students read aloud from a storybook or worksheet. The teacher listens carefully, stopping and correcting students on difficult words. This “guided reading” process is fundamental for both instruction and assessment: It not only helps the kids learn, but the teacher can break the class up into groups with similar reading levels so she can offer tailored lessons.
“Guided reading is needs-based, differentiated instruction and in COVID we couldn’t do it,” said Andrea Burkiett, director of Elementary Curriculum and Instruction at the Savannah-Chatham County Public School System. Breakout sessions are technically possible, “but when you’re talking about a kindergarten student who doesn’t even know how to use a mouse or touchpad, COVID basically made small groups nonexistent.”
Amira replicates the guided reading process by analyzing the child’s speech as they read through a story and identifying things like mispronunciations, skipped words and other common stumbles. It’s based on research going back 20 years that has tested whether learners using such an automated system actually see any gains (and they did, though generally in a lab setting).
In fact I was speaking to Burkiett out of skepticism — “AI” products are thick on the ground and while it does little harm if one recommends you a recipe you don’t like, it’s a serious matter if a kid’s education is impacted. I wanted to be sure this wasn’t a random app hawking old research to lend itself credibility, and after talking with Burkiett and CEO Mark Angel I feel it’s quite the opposite and could actually be a valuable tool for educators. But it needed to convince educators first.
“You have to start by truly identifying the reason for wanting to employ a tech tool,” said Burkiett. “There are a lot of tech tools out there that are exciting, fun for kids, etc., but we could use all of them and not impact growth or learning at all because we didn’t stop and say, this tool helps me with this need.”
Amira was decided on as one that addresses the particular need in the K-5 range of steadily improving reading level through constant practice and feedback.
“When COVID hit, every tech tool came out of the woodwork and was made free and available,” Burkiett recalled. “With Amira you’re looking at a 1:1 tutor at their specific level. She’s not a replacement for a teacher — though it has been that way in COVID — but beyond COVID she could become a force multiplier,” said Burkiett.
You can see the old version of Amira in action below, though it’s been updated since:
Testing Amira with her own district’s students, Burkiett replicated the results that have been obtained in more controlled settings: As much as twice or three times as much progress in reading level based on standard assessment tools, some of which are built into the teacher-side Amira app.
Naturally it isn’t possible to simply attribute all this improvement to Amira — there are other variables in play. But it appears to help and doesn’t hinder, and the effect correlates with frequency of use. The exact mechanism isn’t as important as the fact that kids learn faster when they use the app versus when they don’t, and furthermore this allows teachers to better allocate resources and time. A kid who can’t use it as often because their family shares a single computer is at a disadvantage that has nothing to do with their aptitude — but this problem can be detected and accounted for by the teacher, unlike a simple “read at home” assignment.
“Outside COVID we would always have students struggling with reading, and we would have parents with the money and knowledge to support their student,” Burkiett explained. “But now we can take this tool and offer it to students regardless of mom and dad’s time, mom and dad’s ability to pay. We can now give that tutor session to every single student.”
This is familiar territory for CEO Mark Angel, though the AI aspect, he admits, is new.
“A lot of the Amira team came from Renaissance Learning. bringing fairly conventional edtech software into elementary school classrooms at scale. The actual tech we used was very simple compared to Amira — the big challenge was trying to figure out how to make applications work with the teacher workflow, or make them friendly and resilient when 6-year-olds are your users,” he told me.
“Not to make it trite, but what we’ve learned is really just listen to teachers — they’re the superusers,” Angel continued. “And to design for radically sub-optimal conditions, like background noise, kids playing with the microphone, the myriad things that happen in real-life circumstances.”
Once they were confident in the ability of the app to reliably decode words, the system was given three fundamental tasks that fall under the broader umbrella of machine learning.
The first is telling the difference between a sentence being read correctly and incorrectly. This can be difficult due to the many normal differences between speakers. Singling out errors that matter, versus simply deviation from an imaginary norm (in speech recognition that is often, effectively, American English as spoken by white people) lets readers go at their own pace and in their own voice, with only actual issues like saying a silent k noted by the app.
On that note, considering the prevalence of English language learners with accents, I asked about the company’s performance and approach there. Angel said they and their research partners went to great lengths to make sure they had a representative dataset, and that the model only flags pronunciations that indicate a word was not read or understood correctly.
The second is knowing what action to take to correct an error. In the case of a silent k, it matters whether this is a first grader who is still learning spelling or a fourth grader who is proficient. And is this the first time they’ve made that mistake, or the tenth? Do they need an explanation of why the word is this way, or several examples of similar words? “It’s about helping a student at a moment in time,” Angel said, both in the moment of reading that word, and in the context of their current state as a learner.
Third is a data-based triage system that warns students and parents if a kid may potentially have a language learning disorder like dyslexia. The patterns are there in how they read — and while a system like Amira can’t actually diagnose, it can flag kids who may be high risk to receive a more thorough screening. (A note on privacy: Angel assured me that all information is totally private and by default is considered to belong to the district. “You’d have to be insane to take advantage of it. We’d be out of business in a nanosecond.”)
The $11 million in funding comes at what could be a hockey-stick moment for Amira’s adoption. The round was led by Authentic Ventures II, LP, with participation from Vertical Ventures, Owl Ventures and Rethink Education.
“COVID was a gigantic spotlight on the problem that Amira was created to solve,” Angel said. “We’ve always struggled in this country to help our children become fluent readers. The data is quite scary — more than two-thirds of our fourth graders aren’t proficient readers, and those two-thirds aren’t equally distributed by income or race. It’s a decades-long struggle.”
Having basically given the product away for a year, the company is now looking at how to convert those users into customers. It seems like, just like the rest of society, “going back to normal” doesn’t necessarily mean going back to 2019 entirely. The lessons of the pandemic era are sticking.
“They don’t have the intention to just go back to the old ways,” Angel explained. “They’re searching for a new synthesis — how to incorporate tech, but do it in a classroom with kids elbow to elbow and interacting with teachers. So we’re focused on making Amira the norm in a post-COVID classroom.”
Part of that is making sure the app works with language learners at more levels and grades, so the team is working to expand its capabilities upward to include middle-school students as well as elementary. Another is building out the management side so that success at the classroom and district levels can be more easily understood.
The company is also launching a new app aimed at parents rather than teachers. “A year ago 100% of our usage was in the classroom, then three weeks later 100% of our usage was at home. We had to learn a lot about how to adapt. Out of that learning we’re shipping Amira and the Story Craft that helps parents work with their children.”
Hundreds of districts are on board provisionally — aided by a distribution partnership with Houghton Mifflin Harcourt, also an investor — but decisions are still being kicked down the road as they deal with outbreaks, frustrated parents and every other chaotic aspect of getting back to “normal.”
Perhaps a bit of celebrity juice may help tip the balance in their favor. A new partnership with Miami Dolphins (former Houston Texans) linebacker Brennan Scarlett has the NFL player advising the board and covering the cost of 100 students at a Portland, OR school through his education charity, the Big Yard Foundation — and more to come. It may be a drop in the bucket in the scheme of things, with a year of schooling disrupted, but teachers know that every drop counts.
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One of the issues with deploying a machine learning application is that it tends to be expensive and highly compute intensive. Deeplite, a startup based in Montreal, wants to change that by providing a way to reduce the overall size of the model, allowing it to run on hardware with far fewer resources.
Today, the company announced a $6 million seed investment. Boston-based venture capital firm PJC led the round, with help from Innospark Ventures, Differential Ventures and Smart Global Holdings. Somel Investments, BDC Capital and Desjardins Capital also participated.
Nick Romano, CEO and co-founder at Deeplite, says the company aims to take complex deep neural networks that require a lot of compute power to run, tend to use up a lot of memory and can consume batteries at a rapid pace, and help them run more efficiently with fewer resources.
“Our platform can be used to transform those models into a new form factor to be able to deploy it into constrained hardware at the edge,” Romano explained. Those devices could be as small as a cell phone, a drone or even a Raspberry Pi, meaning that developers could deploy AI in ways that just wouldn’t be possible in most cases right now.
The company has created a product called Neutrino that lets you specify how you want to deploy your model and how much you can compress it to reduce the overall size and the resources required to run it in production. The idea is to run a machine learning application on an extremely small footprint.
Davis Sawyer, chief product officer and co-founder, says that the company’s solution comes into play after the model has been built, trained and is ready for production. Users supply the model and the data set and then they can decide how to build a smaller model. That could involve reducing the accuracy a bit if there is a tolerance for that, but chiefly it involves selecting a level of compression — how much smaller you can make the model.
“Compression reduces the size of the model so that you can deploy it on a much cheaper processor. We’re talking in some cases going from 200 megabytes down to on 11 megabytes or from 50 megabytes to 100 kilobytes,” Davis explained.
Rob May, who is leading the investment for PJC, says that he was impressed with the team and the technology the startup is trying to build.
“Deploying AI, particularly deep learning, on resource-constrained devices, is a broad challenge in the industry with scarce AI talent and know-how available. Deeplite’s automated software solution will create significant economic benefit as Edge AI continues to grow as a major computing paradigm,” May said in a statement.
The idea for the company has roots in the TandemLaunch incubator in Montreal. It launched officially as a company in mid-2019 and today has 15 employees, with plans to double that by the end of this year. As it builds the company, Romano says the founders are focused on building a diverse and inclusive organization.
“We’ve got a strategy that’s going to find us the right people, but do it in a way that is absolutely diverse and inclusive. That’s all part of the DNA of the organization,” he said.
When it’s possible to return to work, the plan is to have offices in Montreal and Toronto that act as hubs for employees, but there won’t be any requirement to come into the office.
“We’ve already discussed that the general approach is going to be that people can come and go as they please, and we don’t think we will need as large an office footprint as we may have had in the past. People will have the option to work remotely and virtually as they see fit,” Romano said.
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As a company founded by data scientists, Streamlit may be in a unique position to develop tooling to help companies build machine learning applications. For starters, it developed an open-source project, but today the startup announced an expanded beta of a new commercial offering and $35 million in Series B funding.
Sequoia led the investment with help from previous investors Gradient Ventures and GGV Capital. Today’s round brings the total raised to $62 million, according to the company.
Data scientists can download the open-source project and build a machine learning application, but it requires a certain level of technical aptitude to make all the parts work. Company co-founder and CEO Adrien Treuille says that so far the company has 20,000 monthly active developers using the open-source tooling to develop streaming apps, which have been viewed millions of times.
As they have gained that traction, they have customers who would prefer to use a commercial service. “It’s great to have something free and that you can use instantly, but not every company is capable of bridging that into a commercial offering,” Treuille explained.
Company COO and co-founder Amanda Kelly says that the commercial offering called Streamlit for Teams is designed to remove some of the complexity around using the open-source application. “The whole [process of] how do I actually deploy an app, put it in a container, make sure it scales, has the resources and is securely connected to data sources […] — that’s a whole different skill set. That’s a DevOps and IT skill set,” she said.
What Streamlit for Teams does is take care of all that in the background for end users, so they can concentrate on the app building part of the equation without help from the technical side of the company to deploy it.
Sonya Huang, a partner at Sequoia, who is leading the firm’s investment in Streamlit, says that she was impressed with the company’s developer focus and sees the new commercial offering as a way to expand usage of the applications that data scientists have been building in the open-source project.
“Streamlit has a chance to define a better interface between data teams and business users by ushering in a new paradigm for interactive, data-rich applications,” Huang said.
They have data scientists at big-name companies like Uber, Delta Dental and John Deere using the open-source product already. They have kept the company fairly lean with 27 employees up until now, but the plan is to double that number in the coming year with the new funding, Kelly says.
She says that the founding team recognizes that it’s important to build a diverse company. She admits that it’s not always easy to do in practice when as a young startup you are just fighting to stay alive, but she says that the funding gives them the luxury to step back and begin to hire more deliberately.
“Literally right before this call, I was on with a consultant who is going to come in and work with the executive team, so that we’re all super clear about what we mean [when it comes to] diversity for us and how is this actually a really core part of our company, so that we can flow that into recruiting and people and engineering practices and and make that a lived value within our company,” she said.
Streamlit for Teams is available in beta starting today. The company plans to make it generally available some time later this year.
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One of the more tedious aspects of machine learning is providing a set of labels to teach the machine learning model what it needs to know. Snorkel AI wants to make it easier for subject matter experts to apply those labels programmatically, and today the startup announced a $35 million Series B.
It also announced a new tool called Application Studio that provides a way to build common machine learning applications using templates and predefined components.
Lightspeed Venture Partners led the round with participation from previous investors Greylock, GV, In-Q-Tel and Nepenthe Capital. New investors Walden and BlackRock also joined in. The startup reports that it has now raised $50 million.
Company co-founder and CEO Alex Ratner says that data labeling remains a huge challenge and roadblock to moving machine learning and artificial intelligence forward inside a lot of industries because it is costly, labor-intensive and hard for the subject experts to carve out the time to do it.
“The not so hidden secret about AI today is that in spite of all the technological and tooling advancements, roughly 80 to 90% of the cost and time for an average AI project goes into just manually labeling and collecting and relabeling this training data,” he said.
He says that his company has developed a solution to simplify this process to make it easier for subject experts to programmatically add the labels, a process he says decreases the time and effort required to apply labels in a pretty dramatic way from months to hours or days, depending on the complexity of the data.
As the company has developed this methodology, customers have been asking for help in the next step of the machine learning process, which is taking that training data and the model and building an application. That’s where the Application Studio comes in. It could be a contract classifier at a bank or a network anomaly detector at a telco and it helps companies take that next step after data labeling.
“It’s not just about how you programmatically label the data, it’s also about the models, the preprocessors, the post processors, and so we’ve made this now accessible in a kind of templated and visual no-code interface,” he said.
The company’s products are based on research that began at the Stanford AI Lab in 2015. The founders spent four years in the research phase before launching Snorkel in 2019. Today, the startup has 40 employees. Ratner recognizes the issues that the technology industry has had from a diversity perspective and says he has made a conscious effort to build a diverse and inclusive company.
“What I can say is that we tried to prioritize it at a company level, the full team level and at a board level from day one, and to also put action behind that. So we’ve been working with external firms for internal training and audits and strategy around DEI, and we’ve made pipeline diversity a non-negotiable requirement of any of our contracts with recruiting firms,” he said.
Ratner also recognizes that automation can hard code bias into machine learning models, and he’s hopeful that by simplifying the labeling process, it can make it much easier to detect bias when it happens.
“If you start with a dozen or two dozen of what we call labeling functions in Snorkel, you still need to be vigilant and proactive about trying to detect bias, but it’s easier to audit what taught your model to change it by just going back and looking at a couple of hundred lines of code.”
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Hiro Capital has gradually been making a name for itself as an investor in the area know as “Digital Sports” or DSports for short. It’s now led a $2.3 million funding round in PlayerData. While the round might sound small, the area it’s going into is large and growing. Also investing in the round is Sir Terry Leahy, previously the CEO of Tesco, the largest British retailer.
Edinburgh, U.K.-based PlayerData uses wearable technology and software tracking to give grass-roots and professional sports teams feedback on their training. It can, for instance, allow coaches to replay key moments from a game, even modeling different outcomes based on player positioning.
This is Hiro Capital’s fourth DSports and “connected fitness” investment, and it joins Zwift, FitXR and NURVV. Hiro has also invested in eight games startups in the U.K., U.S. and Europe, as befits the heritage of co-founder and partner Ian Livingstone, OBE, CBE, who is the former chairman of Tomb Raider publisher Eidos plc and all-round gaming pioneer.
PlayerData says it has captured more than 10,000 team sessions across U.K. soccer and rugby, and logged over 50 million meters of play. It also has strong network effects, it says. Every time a new team encounters one using Playerdata’s platform, it generates five more clubs as users.
Roy Hotrabhvanon is co-founder and CEO of PlayerData, and is a former international-level archer. He’s joined by Hayden Ball, co-founder and CTO, a firmware and cloud infrastructure expert.
PlayerData app. Image Credits: PlayerData
In a statement Hotrabhvanon said: “Our mission is to bring fine-grained data and insight to clubs across team sports, helping them supercharge their game-making, improve player performance, and avoid injury… Our ultimate goal is to implement cutting-edge insights from pioneering wearables that are applicable to any team in any discipline at any level.”
Cherry Freeman, co-founding partner at Hiro, says: “PlayerData ticks all of our key boxes: a huge TAM with over 3 million grass-roots clubs; a deep moat built on shared player data, machine learning and highly actionable predictive algorithms; compelling customer network effects; and a really impressive yet humble founding team.”
The PlayerData news forms part of a wider growth in digital sports, which includes such breakout names as Peloton, Tonal, Mirror and Hiro’s portfolio investment, Zwift. With the pandemic putting an emphasis on both home workouts and general health, the fascination with digital measurement of performance now has a growing grip on the sector.
Speaking to TechCrunch, Freeman added: “We think there are something like 3 million teams that are potential customers for PlayerData. Obviously the number of runners is enormous, and they only need to get a small slice of that market to have a very, very large business. At the end of the day everyone, everyone works out, even if you just go for a walk, so the target market’s huge and they started with running but their technology is applicable to a whole raft of other sports.”
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Machine learning (ML) models are only as good as the data you feed them. That’s true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it’s imperative that businesses monitor their data pipelines for these systems.
That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.
Aporia co-founder and CEO Liran Hason, after five years with the Israel Defense Forces, previously worked on the data science team at Adallom, a security company that was acquired by Microsoft in 2015. After the sale, he joined venture firm Vertex Ventures before starting Aporia in late 2019. But it was during his time at Adallom where he first encountered the problems that Aporio is now trying to solve.
“I was responsible for the production architecture of the machine learning models,” he said of his time at the company. “So that’s actually where, for the first time, I got to experience the challenges of getting models to production and all the surprises that you get there.”
The idea behind Aporia, Hason explained, is to make it easier for enterprises to implement machine learning models and leverage the power of AI in a responsible manner.
“AI is a super powerful technology,” he said. “But unlike traditional software, it highly relies on the data. Another unique characteristic of AI, which is very interesting, is that when it fails, it fails silently. You get no exceptions, no errors. That becomes really, really tricky, especially when getting to production, because in training, the data scientists have full control of the data.”
But as Hason noted, a production system may depend on data from a third-party vendor and that vendor may one day change the data schema without telling anybody about it. At that point, a model — say for predicting whether a bank’s customer may default on a loan — can’t be trusted anymore, but it may take weeks or months before anybody notices.
Aporia constantly tracks the statistical behavior of the incoming data and when that drifts too far away from the training set, it will alert its users.
One thing that makes Aporia unique is that it gives its users an almost IFTTT or Zapier-like graphical tool for setting up the logic of these monitors. It comes pre-configured with more than 50 combinations of monitors and provides full visibility in how they work behind the scenes. That, in turn, allows businesses to fine-tune the behavior of these monitors for their own specific business case and model.
Initially, the team thought it could build generic monitoring solutions. But the team realized that this wouldn’t only be a very complex undertaking, but that the data scientists who build the models also know exactly how those models should work and what they need from a monitoring solution.
“Monitoring production workloads is a well-established software engineering practice, and it’s past time for machine learning to be monitored at the same level,” said Rona Segev, founding partner at TLV Partners. “Aporia‘s team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust.”
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For this morning’s column, Alex Wilhelm looked back on the last few months, “a busy season for technology exits” that followed a hot Q4 2020.
We’re seeing signs of an IPO market that may be cooling, but even so, “there are sufficient SPACs to take the entire recent Y Combinator class public,” he notes.
Once we factor in private equity firms with pockets full of money, it’s evident that late-stage companies have three solid choices for leveling up.
Seeking more insight into these liquidity options, Alex interviewed:
After recapping their deals, each executive explains how their company determined which flashing red “EXIT” sign to follow. As Alex observed, “choosing which option is best from a buffet’s worth of possibilities is an interesting task.”
Thanks very much for reading Extra Crunch! Have a great weekend.
Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist
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Image Credits: Nigel Sussman
On Tuesday, we published a four-part series on Tonal, a home fitness startup that has raised $200 million since it launched in 2018. The company’s patented hardware combines digital weights, coaching and AI in a wall-mounted system that sells for $2,995.
By any measure, it is poised for success — sales increased 800% between December 2019 and 2020, and by the end of this year, the company will have 60 retail locations. On Wednesday, Tonal reported a $250 million Series E that valued the company at $1.6 billion.
Our deep dive examines Tonal’s origins, product development timeline, its go-to-market strategy and other aspects that combined to spark investor interest and customer delight.
We call this format the “EC-1,” since these stories are as comprehensive and illuminating as the S-1 forms startups must file with the SEC before going public.
Here’s how the Tonal EC-1 breaks down:
We have more EC-1s in the works about other late-stage startups that are doing big things well and making news in the process.
Image Credits: Nigel Sussman (opens in a new window)
Why did Deliveroo struggle when it began to trade? Is it suffering from cultural dissonance between its high-growth model and more conservative European investors?
Let’s peek at the numbers and find out.
Image Credits: Nigel Sussman (opens in a new window)
The Exchange doubts many folks expected the IPO climate to get so chilly without warning. But we could be in for a Q2 pause in the formerly scorching climate for tech debuts.
Image Credits: Nigel Sussman (opens in a new window)
A $65 million Series B is remarkable, even by 2021 standards. But the fact that a16z is pouring more capital into the alt-media space is not a surprise.
Substack is a place where publications have bled some well-known talent, shifting the center of gravity in media. Let’s take a look at Substack’s historical growth.
Image Credits: Visual Generation / Getty Images
Robotic process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.
RPA has enabled executives to provide a level of automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.
Image Credits: Javier Zayas Photography (opens in a new window) / Getty Images
This year is all about the roll-ups, the aggregation of smaller companies into larger firms, creating a potentially compelling path for equity value. The interest in creating value through e-commerce brands is particularly striking.
Just a year ago, digitally native brands had fallen out of favor with venture capitalists after so many failed to create venture-scale returns. So what’s the roll-up hype about?
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The cyber world has entered a new era in which attacks are becoming more frequent and happening on a larger scale than ever before. Massive hacks affecting thousands of high-level American companies and agencies have dominated the news recently. Chief among these are the December SolarWinds/FireEye breach and the more recent Microsoft Exchange server breach.
Everyone wants to know: If you’ve been hit with the Exchange breach, what should you do?
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Machine learning has become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.
But for engineering and team leaders without an ML background, this can also feel overwhelming and intimidating.
Here are best practices and must-know components broken down into five practical and easily applicable lessons.
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Embedded procurement is the natural evolution of embedded fintech.
In this next wave, businesses will buy things they need through vertical B2B apps, rather than through sales reps, distributors or an individual merchant’s website.
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There’s a persistent fallacy swirling around that any startup growing pain or scaling problem can be solved with business development.
That’s frankly not true.
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Dear Sophie:
I’m a founder of a startup on an E-2 investor visa and just got engaged! My soon-to-be spouse will sponsor me for a green card.
Are there any minimum salary requirements for her to sponsor me? Is there anything I should keep in mind before starting the green card process?
— Betrothed in Belmont
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Many organizations perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.
That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage.
Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.
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Cyber strategy and company strategy are inextricably linked. Consequently, chief information security officers in the C-suite will be just as common and influential as CFOs in maximizing shareholder value.
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Edtech unicorns have boatloads of cash to spend following the capital boost to the sector in 2020. As a result, edtech M&A activity has continued to swell.
The idea of a well-capitalized startup buying competitors to complement its core business is nothing new, but exits in this sector are notable because the money used to buy startups can be seen as an effect of the pandemic’s impact on remote education.
But in the past week, the consolidation environment made a clear statement: Pandemic-proven startups are scooping up talent — and fast.
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Knowledge transfer is not the only trend flowing in the U.S.-Asia-LatAm nexus. Competition is afoot as well.
Because of similar market conditions, Asian tech giants are directly expanding into Mexico and other LatAm countries.
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There’s certainly no shortage of SaaS performance metrics leaders focus on, but NRR (net revenue retention) is without question the most underrated metric out there.
NRR is simply total revenue minus any revenue churn plus any revenue expansion from upgrades, cross-sells or upsells. The greater the NRR, the quicker companies can scale.
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Even the most experienced and talented game designers from the mobile F2P business usually fail to understand what features matter to Robloxians.
For those just starting their journey in Roblox game development, these are the most common mistakes gaming professionals make on Roblox.
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“Lead with love, and the money comes.” It’s one of the cornerstone values at Poshmark. On the latest episode of Extra Crunch Live, Chandra and Chaddha sat down with us and walked us through their original Series A pitch deck.
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Cities are bustling hubs where people live, work and play. When the pandemic hit, some people fled major metropolitan markets for smaller towns — raising questions about the future validity of cities.
But those who predicted that COVID-19 would destroy major urban communities might want to stop shorting the resilience of these municipalities and start going long on what the post-pandemic future looks like.
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There’s plenty of uncertainty surrounding copyright issues, fraud and adult content, and legal implications are the crux of the NFT trend.
Whether a court would protect the receipt-holder’s ownership over a given file depends on a variety of factors. All of these concerns mean artists may need to lawyer up.
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It’s a reasonable question: Why would anyone pay that much for Cazoo today if Carvana is more profitable and whatnot? Well, growth. That’s the argument anyway.
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Self-driving and robotics startup Cartken has partnered with REEF Technology, a startup that operates parking lots and neighborhood hubs, to bring self-driving delivery robots to the streets of downtown Miami.
With this announcement, Cartken officially comes out of stealth mode. The company, founded by ex-Google engineers and colleagues behind the unrequited Bookbot, was formed to develop market-ready tech in self-driving, AI-powered robotics and delivery operations in 2019, but the team has kept operations under wraps until now. This is Cartken’s first large deployment of self-driving robots on sidewalks.
After a few test months, the REEF-branded electric-powered robots are now delivering dinner orders from REEF’s network of delivery-only kitchens to people located within a 3/4-mile radius in downtown Miami. The robots, which are insulated and thus can preserve the heat of a plate of spaghetti or other hot food, are pre-stationed at designated logistics hubs and dispatched with orders for delivery as the food is prepared.
“We want to show how future-forward Miami can be,” Matt Lindenberger, REEF’s chief technology officer, told TechCrunch. “This is a great chance to show off the capabilities of the tech. The combination of us having a big presence in Miami, the fact that there are a lot of challenges around congestion as COVID subsides, still shows a really good environment where we can show how this tech can work.”
Lindenberg said Miami is a great place to start, but it’s just the beginning, with potential for the Cartken robots to be used for REEF’s other last-mile delivery businesses. Currently, only two restaurant delivery robots are operating in Miami, but Lindenberger said the company is planning to expand further into the city and outward into Fort Lauderdale, as well as other large metros the company operates in, such as Dallas, Atlanta, Los Angeles and eventually New York.
Lindenberger is hoping the presence of robots in the streets can act as a “force multiplier,” allowing them to scale while maintaining quality of service in a cost-effective way.
“We’re seeing an explosion in deliveries right now in a post-pandemic world and we foresee that to continue, so these types of no-contact, zero-emission automation techniques are really critical,” he said.
Cartken’s robots are powered by a combination of machine learning and rules-based programming to react to every situation that could occur, even if that just means safely stopping and asking for help, Christian Bersch, CEO of Cartken, told TechCrunch. REEF would have supervisors on site to remotely control the robot if needed, a caveat that was included in the 2017 legislation that allowed for the operation of self-driving delivery robots in Florida.
“The technology at the end of the day is very similar to that of a self-driving car,” said Bersch. “The robot is seeing the environment, planning around obstacles like pedestrians or lampposts. If there’s an unknown situation, someone can help the robot out safely because it can stop on a dime. But it’s important to also have that level of autonomy on the robot because it can react in a split second, faster than anybody remotely could, if something happens like someone jumps in front of it.”
REEF marks specific operating areas on the map for the robots and Cartken tweaks the configuration for the city, accounting for specific situations a robot might need to deal with, so that when the robots are given a delivery address, they can make moves and operate like any other delivery driver. Only this driver has an LTE connection and is constantly updating its location so REEF can integrate it into its fleet management capabilities.
Eventually, Lindenberger said, they’re hoping to be able to offer the option for customers to choose robot delivery on the major food delivery platforms REEF works with like Postmates, UberEats, DoorDash or GrubHub. Customers would receive a text when the robot arrives so they could go outside and meet it. However, the tech is not quite there yet.
Currently the robots only make it street-level, and then the food is passed off to a human who delivers it directly to the door, which is a service that most customers prefer. Navigating into an apartment complex and to a customer’s unit is difficult for a robot to manage just yet, and many customers aren’t quite ready to interact directly with a robot.
“It’s an interim step, but this was a path for us to move forward quickly with the technology without having any other boundaries,” said Lindenberger. “Like with any new tech, you want to take it in steps. So a super important step which we’ve now taken and works very well is the ability to dispatch robots within a certain radius and know that they’re going to arrive there. That in and of itself is a huge step and it allows us to learn what kind of challenges you have in terms of that very last step. Then we can begin to work with Cartken to solve that last piece. It’s a big step just being able to do this automation.”
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