Jan
08
2021
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Extra Crunch roundup: 2 VC surveys, Tesla’s melt up, The Roblox Gambit, more

This has been quite a week.

Instead of walking backward through the last few days of chaos and uncertainty, here are three good things that happened:

  • Google employee Sara Robinson combined her interest in machine learning and baking to create AI-generated hybrid treats.
  • A breakthrough could make water desalination 30%-40% more effective.
  • Bianca Smith will become the first Black woman to coach a professional baseball team.

Despite many distractions in our first full week of the new year, we published a full slate of stories exploring different aspects of entrepreneurship, fundraising and investing.

We’ve already gotten feedback on this overview of subscription pricing models, and a look back at 2020 funding rounds and exits among Israel’s security startups was aimed at our new members who live and work there, along with international investors who are seeking new opportunities.

Plus, don’t miss our first investor surveys of 2021: one by Lucas Matney on social gaming, and another by Mike Butcher that gathered responses from Portugal-based investors on a wide variety of topics.

Thanks very much for reading Extra Crunch this week. I hope we can all look forward to a nice, boring weekend with no breaking news alerts.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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The Roblox Gambit

In February 2020, gaming platform Roblox was valued at $4 billion, but after announcing a $520 million Series H this week, it’s now worth $29.5 billion.

“Sure, you could argue that Roblox enjoyed an epic 2020, thanks in part to COVID-19,” writes Alex Wilhelm this morning. “That helped its valuation. But there’s a lot of space between $4 billion and $29.5 billion.”

Alex suggests that Roblox’s decision to delay its IPO and raise an enormous Series H was a grandmaster move that could influence how other unicorns will take themselves to market. “A big thanks to the gaming company for running this experiment for us.”

I asked him what inspired the headline; like most good ideas, it came to him while he was trying to get to sleep.

“I think that I had ‘The Queen’s Gambit’ somewhere in my head, so that formed the root of a little joke with myself. Roblox is making a strategic wager on method of going public. So, ‘gambit’ seems to fit!”

8 investors discuss social gaming’s biggest opportunities

girl playing games on desktop computer

Image Credits: Erik Von Weber (opens in a new window) / Getty Images

For our first investor survey of the year, Lucas Matney interviewed eight VCs who invest in massively multiplayer online games to discuss 2021 trends and opportunities:

  • Hope Cochran, Madrona Venture Group
  • Daniel Li, Madrona Venture Group
  • Niko Bonatsos, General Catalyst
  • Ethan Kurzweil, Bessemer Venture Partners
  • Sakib Dadi, Bessemer Venture Partners
  • Jacob Mullins, Shasta Ventures
  • Alice Lloyd George, Rogue
  • Gigi Levy-Weiss, NFX

Having moved far beyond shooters and sims, platforms like Twitch, Discord and Fortnite are “where culture is created,” said Daniel Li of Madrona.

Rep. Alexandria Ocasio-Cortez uses Twitch to explain policy positions, major musicians regularly perform in-game concerts on Fortnite and in-game purchases generated tens of billions last year.

“Gaming is a unique combination of science and art, left and right brain,” said Gigi Levy-Weiss of NFX. “It’s never just science (i.e., software and data), which is why many investors find it hard.”

How to convert customers with subscription pricing

Giant hand and magnet picking up office and workers

Image Credits: C.J. Burton (opens in a new window) / Getty Images

Startups that lack insight into their sales funnel have high churn, low conversion rates and an inability to adapt or leverage changes in customer behavior.

If you’re hoping to convert and retain customers, “reinforcing your value proposition should play a big part in every level of your customer funnel,” says Joe Procopio, founder of Teaching Startup.

What is up with Tesla’s value?

Elon Musk, founder of SpaceX and chief executive officer of Tesla Inc., arrives at the Axel Springer Award ceremony in Berlin, Germany, on Tuesday, Dec. 1, 2020. Tesla Inc. will be added to the S&P 500 Index in one shot on Dec. 21, a move that will ripple through the entire market as money managers adjust their portfolios to make room for shares of the $538 billion company. Photographer: Liesa Johannssen-Koppitz/Bloomberg via Getty Images

Image Credits: Bloomberg (opens in a new window) / Getty Images

Alex Wilhelm followed up his regular Friday column with another story that tries to find a well-grounded rationale for Tesla’s sky-high valuation of approximately $822 billion.

Meanwhile, GM just unveiled a new logo and tagline.

As ever, I learned something new while editing: A “melt up” occurs when investors start clamoring for a particular company because of acute FOMO (the fear of missing out).

Delivering 500,000 cars in 2020 was “impressive,” says Alex, who also acknowledged the company’s ability to turn GAAP profits, but “pride cometh before the fall, as does a melt up, I think.”

Note: This story has Alex’s original headline, but I told him I would replace the featured image with a photo of someone who had very “richest man in the world” face.

How Segment redesigned its core systems to solve an existential scaling crisis

Abstract glowing grid and particles

Image Credits: piranka / Getty Images

On Tuesday, enterprise reporter Ron Miller covered a major engineering project at customer data platform Segment called “Centrifuge.”

“Its purpose was to move data through Segment’s data pipes to wherever customers needed it quickly and efficiently at the lowest operating cost,” but as Ron reports, it was also meant to solve “an existential crisis for the young business,” which needed a more resilient platform.

Dear Sophie: Banging my head against the wall understanding the US immigration system

Image Credits: Sophie Alcorn

Dear Sophie:

Now that the U.S. has a new president coming in whose policies are more welcoming to immigrants, I am considering coming to the U.S. to expand my company after COVID-19. However, I’m struggling with the morass of information online that has bits and pieces of visa types and processes.

Can you please share an overview of the U.S. immigration system and how it works so I can get the big picture and understand what I’m navigating?

— Resilient in Romania

The first “Dear Sophie” column of each month is available on TechCrunch without a paywall.

Revenue-based financing: The next step for private equity and early-stage investment

Shot of a group of people holding plants growing out of soil

Image Credits: Hiraman (opens in a new window) / Getty Images

For founders who aren’t interested in angel investment or seeking validation from a VC, revenue-based investing is growing in popularity.

To gain a deeper understanding of the U.S. RBI landscape, we published an industry report on Wednesday that studied data from 134 companies, 57 funds and 32 investment firms before breaking out “specific verticals and business models … and the typical profile of companies that access this form of capital.”

Lisbon’s startup scene rises as Portugal gears up to be a European tech tiger

Man using laptop at 25th of April Bridge in Lisbon, Portugal

Image Credits: Westend61 (opens in a new window)/ Getty Images

Mike Butcher continues his series of European investor surveys with his latest dispatch from Lisbon, where a nascent startup ecosystem may get a Brexit boost.

Here are the Portugal-based VCs he interviewed:

  • Cristina Fonseca, partner, Indico Capital Partners
  • Pedro Ribeiro Santos, partner, Armilar Venture Partners
  • Tocha, partner, Olisipo Way
  • Adão Oliveira, investment manager, Portugal Ventures
  • Alexandre Barbosa, partner, Faber
  • António Miguel, partner, Mustard Seed MAZE
  • Jaime Parodi Bardón, partner, impACT NOW Capital
  • Stephan Morais, partner, Indico Capital Partners
  • Gavin Goldblatt, managing partner, Portugal Gateway

How late-stage edtech companies are thinking about tutoring marketplaces

Life Rings flying out beneath storm clouds are a metaphor for rescue, help and aid.

Image Credits: John Lund (opens in a new window)/ Getty Images

How do you scale online tutoring, particularly when demand exceeds the supply of human instructors?

This month, Chegg is replacing its seven-year-old marketplace that paired students with tutors with a live chatbot.

A spokesperson said the move will “dramatically differentiate our offerings from our competitors and better service students,” but Natasha Mascarenhas identified two challenges to edtech automation.

“A chatbot won’t work for a student with special needs or someone who needs to be handheld a bit more,” she says. “Second, speed tutoring can only work for a specific set of subjects.”

Decrypted: How bad was the US Capitol breach for cybersecurity?

Image Credits: Treedeo (opens in a new window) / Getty Images

While I watched insurrectionists invade and vandalize the U.S. Capitol on live TV, I noticed that staffers evacuated so quickly, some hadn’t had time to shut down their computers.

Looters even made off with a laptop from Senator Jeff Merkley’s office, but according to security reporter Zack Whittaker, the damages to infosec wasn’t as bad as it looked.

Even so, “the breach will likely present a major task for Congress’ IT departments, which will have to figure out what’s been stolen and what security risks could still pose a threat to the Capitol’s network.”

Extra Crunch’s top 10 stories of 2020

On New Year’s Eve, I made a list of the 10 “best” Extra Crunch stories from the previous 12 months.

My methodology was personal: From hundreds of posts, these were the 10 I found most useful, which is my key metric for business journalism.

Some readers are skeptical about paywalls, but without being boastful, Extra Crunch is a premium product, just like Netflix or Disney+. I know, we’re not as entertaining as a historical drama about the reign of Queen Elizabeth II or a space western about a bounty hunter. But, speaking as someone who’s worked at several startups, Extra Crunch stories contain actionable information you can use to build a company and/or look smart in meetings — and that’s worth something.

Dec
31
2020
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How artificial intelligence will be used in 2021

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.

Dec
10
2020
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Boast.ai raises $23M to help businesses get their R&D tax credits

Nobody likes dealing with taxes — until the system works in your favor. In many countries, startups can receive tax credits for their R&D work and related employee cost, but as with all things bureaucracy, that’s often a slow and onerous task. Boast.ai aims to make this process far easier, by using a mix of AI and tax experts. The company, which currently has about 1,000 customers, today announced that it has raised a $23 million Series A round led by Radian Capital.

Launched in 2012 by co-founders Alex Popa (CEO) and Lloyed Lobo (president), Boast focuses on helping companies — and especially startups — in the U.S. and Canada claim their R&D tax credits.

“Globally, over $200 billion has been given in R&D incentives to fund businesses, not only in the U.S. and Canada, but the U.K., Australia, France, New Zealand, Ireland give out these incentives,” Lobo explained. “But there’s huge red tape. It’s a cumbersome process. You got to dive in and figure out work that qualifies and what doesn’t. Then you’ve got to file it with your taxes. Then if the government audits you, it’s like a long, laborious process.”

Image Credits: Boast.ai

After working on a few other startup ideas, the co-founders decided to go all-in on Boast. And in the process of working on other ideas, they also realized that AI wasn’t going to be able to do it all, but that it was getting good enough to augment humans to make a complex process like dealing with R&D tax credits scalable.

“The way I think to bootstrap a company is three things,” Lobo explained. “One, customers are looking for an outcome. Get them that outcome in the fastest, cheapest way possible. Two, when you’re doing that, you may have to do a lot of manual work. Figure out what those manual touch points are and then build the workflow to automate that. And once you have those two things, then you’ll have enough data to start working on artificial intelligence and machine learning. Those are the key learnings that we learned the hard way.”

So after doing some of that manual work, Boast can now automatically pull in data using tech tools like JIRA and GitHub and a company’s financial tools like QuickBooks, Gusto and (soon) ADP. It then uses its algorithms to cluster this data, figure out how much time employees spend on projects that would qualify for a tax credit and automate the tax filing process. Throughout the process — and to interact with the government if necessary — the company keeps humans in the loop.

“So all our [customer success] team is engineers,” Lobo noted. “Because if you don’t have engineers they can’t inform the decision-making process. They help figure out if there are any loose ends and then they deal with the audits, communicating with the government and whatnot. That’s how we’re able to effectively get SaaS-like margins or more.”

Ideally, a tool like Boast pays for itself and the company says it has secured more than $150 million in R&D tax credits since launch. Currently, it’s also doubling growth year over year, and that’s what made the founders decide to raise outside money for the first time. That funding will go toward increasing the sales team (which is currently only four people strong) and improving the platform, but Lobo was clear that he doesn’t want to be too aggressive. The goal, he said, is not to have to raise again until Boast can hit the $30 to $50 million revenue mark.

Once fully implemented, Boast also effectively becomes a system of record for all R&D and engineering data. And indeed, that’s the company’s overall vision, with the tax credits being somewhat of a Trojan horse to get to this point. By the middle of next year, the team plans to offer a new product around R&D-based financing, Lobo tells me.

Over the years, the Boast team also focused on not just growing its customer base but also the overall startup ecosystem in the markets in which it operates, with a special focus on Canada. The Boast team, for example, is also the team behind the popular annual Traction conference in Vancouver, Canada (Disclosure: I’ve moderated sessions at the event since its inception). A thriving startup ecosystem creates a larger client base for Boast, too, after all — and coincidently, the team met its investors at the event, too.

Dec
09
2020
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Arthur.ai snags $15M Series A to grow machine learning monitoring tool

At a time when more companies are building machine learning models, Arthur.ai wants to help by ensuring the model accuracy doesn’t begin slipping over time, thereby losing its ability to precisely measure what it was supposed to. As demand for this type of tool has increased this year, in spite of the pandemic, the startup announced a $15 million Series A today.

The investment was led by Index Ventures with help from newcomers Acrew and Plexo Capital, along with previous investors Homebrew, AME Ventures and Work-Bench. The round comes almost exactly a year after its $3.3 million seed round.

As CEO and co-founder Adam Wenchel explains, data scientists build and test machine learning models in the lab under ideal conditions, but as these models are put into production, the performance can begin to deteriorate under real-world scrutiny. Arthur.ai is designed to root out when that happens.

Even as COVID has wreaked havoc throughout much of this year, the company has grown revenue 300% in the last six months smack dab in the middle of all that. “Over the course of 2020, we have begun to open up more and talk to [more] customers. And so we are starting to get some really nice initial customer traction, both in traditional enterprises as well as digital tech companies,” Wenchel told me. With 15 customers, the company is finding that the solution is resonating with companies.

It’s interesting to note that AWS announced a similar tool yesterday at re:Invent called SageMaker Clarify, but Wenchel sees this as more of a validation of what his startup has been trying to do, rather than an existential threat. “I think it helps create awareness, and because this is our 100% focus, our tools go well beyond what the major cloud providers provide,” he said.

Investor Mike Volpi from Index certainly sees the value proposition of this company. “One of the most critical aspects of the AI stack is in the area of performance monitoring and risk mitigation. Simply put, is the AI system behaving like it’s supposed to?” he wrote in a blog post announcing the funding.

When we spoke a year ago, the company had eight employees. Today it has 17 and it expects to double again by the end of next year. Wenchel says that as a company whose product looks for different types of bias, it’s especially important to have a diverse workforce. He says that starts with having a diverse investment team and board makeup, which he has been able to achieve, and goes from there.

“We’ve sponsored and work with groups that focus on both general sort of coding for different underrepresented groups as well as specifically AI, and that’s something that we’ll continue to do. And actually I think when we can get together for in-person events again, we will really go out there and support great organizations like AI for All and Black Girls Code,” he said. He believes that by working with these groups, it will give the startup a pipeline to underrepresented groups, which they can draw upon for hiring as the needs arise.

Wenchel says that when he can go back to the office, he wants to bring employees back, at least for part of the week for certain kinds of work that will benefit from being in the same space.

Dec
08
2020
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AWS expands on SageMaker capabilities with end-to-end features for machine learning

Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.

As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.

“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”

Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.

The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.

Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.

Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.

To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.

Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.

Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.

Dec
08
2020
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AWS announces SageMaker Clarify to help reduce bias in machine learning models

As companies rely increasingly on machine learning models to run their businesses, it’s imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models.

“We are launching Amazon SageMaker Clarify. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle,” Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch.

He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model.

“Once I have my training data set, I can [look at things like if I have] an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance,” Saha explained.

After you build your model, you can run SageMaker Clarify again to look for similar factors that might have crept into your model as you built it. “So you start off by doing statistical bias analysis on your data, and then post training you can again do analysis on the model,” he said.

There are multiple types of bias that can enter a model due to the background of the data scientists building the model, the nature of the data and how they data scientists interpret that data through the model they built. While this can be problematic in general it can also lead to racial stereotypes being extended to algorithms. As an example, facial recognition systems have proven quite accurate at identifying white faces, but much less so when it comes to recognizing people of color.

It may be difficult to identify these kinds of biases with software as it often has to do with team makeup and other factors outside the purview of a software analysis tool, but Saha says they are trying to make that software approach as comprehensive as possible.

“If you look at SageMaker Clarify it gives you data bias analysis, it gives you model bias analysis, it gives you model explainability it gives you per inference explainability it gives you a global explainability,” Saha said.

Saha says that Amazon is aware of the bias problem and that is why it created this tool to help, but he recognizes that this tool alone won’t eliminate all of the bias issues that can crop up in machine learning models, and they offer other ways to help too.

“We are also working with our customers in various ways. So we have documentation, best practices, and we point our customers to how to be able to architect their systems and work with the system so they get the desired results,” he said.

SageMaker Clarify is available starting to day in multiple regions.

Dec
07
2020
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Tecton.ai nabs $35M Series B as it releases machine learning feature store

Tecton.ai, the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A.

When we spoke to the company in April, it was working with early customers in a beta version of the product, but today, in addition to the funding, they are also announcing the general availability of the platform.

As with their Series A, this round has Andreessen Horowitz and Sequoia Capital co-leading the investment. The company has now raised $60 million.

The reason these two firms are so committed to Tecton is the specific problem around machine learning the company is trying to solve. “We help organizations put machine learning into production. That’s the whole goal of our company, helping someone build an operational machine learning application, meaning an application that’s powering their fraud system or something real for them […] and making it easy for them to build and deploy and maintain,” company CEO and co-founder Mike Del Balso explained.

They do this by providing the concept of a feature store, an idea they came up with and which is becoming a machine learning category unto itself. Just last week, AWS announced the Sagemaker Feature store, which the company saw as major validation of their idea.

As Tecton defines it, a feature store is 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 this works hand-in-hand with the other layers of a machine learning stack. “When you build a machine learning application, you use a machine learning stack that could include a model training system, maybe a model serving system or an MLOps kind of layer that does all the model management, and then you have a feature management layer, a feature store which is us — and so we’re an end-to-end life cycle for the data pipelines,” he said.

With so much money behind the company it is growing fast, going from 17 employees to 26 since we spoke in April, with plans to more than double that number by the end of next year. Del Balso says he and his co-founders are committed to building a diverse and inclusive company, but he acknowledges it’s not easy to do.

“It’s actually something that we have a primary recruiting initiative on. It’s very hard, and it takes a lot of effort, it’s not something that you can just make like a second priority and not take it seriously,” he said. To that end, the company has sponsored and attended diversity hiring conferences and has focused its recruiting efforts on finding a diverse set of candidates, he said.

Unlike a lot of startups we’ve spoken to, Del Balso wants to return to an office setup as soon as it is feasible to do so, seeing it as a way to build more personal connections between employees.

Dec
01
2020
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AWS adds natural language search service for business intelligence from its data sets

When Amazon Web Services launched QuickSight, its business intelligence service, back in 2016 the company wanted to provide product information and customer information for business users — not just developers.

At the time, the natural language processing technologies available weren’t robust enough to give customers the tools to search databases effectively using queries in plain speech.

Now, as those technologies have matured, Amazon is coming back with a significant upgrade called QuickSight Q, which allows users to just ask a simple question and get the answers they need, according to Andy Jassy’s keynote at AWS re:Invent.

“We will provide natural language to provide what we think the key learning is,” said Jassy. “I don’t like that our users have to know which databases to access or where data is stored. I want them to be able to type into a search bar and get the answer to a natural language question.

That’s what QuickSight Q aims to do. It’s a direct challenge to a number of business intelligence startups and another instance of the way machine learning and natural language processing are changing business processes across multiple industries.

“The way Q works. Type in a question in natural language [like]… ‘Give me the trailing twelve month sales of product X?’… You get an answer in seconds. You don’t have to know tables or have to know data stores.”

It’s a compelling use case and gets at the way AWS is integrating machine learning to provide more no-code services to customers. “Customers didn’t hire us to do machine learning,” Jassy said. “They hired us to answer the questions.”

Nov
19
2020
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FireEye acquires Respond Software for $186M, announces $400M investment

The security sector is ever frothy and acquisitive. Just last week Palo Alto Networks grabbed Expanse for $800 million. Today it was FireEye’s turn, snagging Respond Software, a company that helps customers investigate and understand security incidents, while reducing the need for highly trained (and scarce) security analysts. The deal has closed, according to the company.

FireEye had its eye on Respond’s Analyst product, which it plans to fold into its Mandiant Solutions platform. Like many companies today, FireEye is focused on using machine learning to help bolster its solutions and bring a level of automation to sorting through the data, finding real issues and weeding out false positives. The acquisition gives them a quick influx of machine learning-fueled software.

FireEye sees a product that can help add speed to its existing tooling. “With Mandiant’s position on the front lines, we know what to look for in an attack, and Respond’s cloud-based machine learning productizes our expertise to deliver faster outcomes and protect more customers,” Kevin Mandia, FireEye CEO said in a statement announcing the deal.

Mike Armistead, CEO at Respond, wrote in a company blog post that today’s acquisition marks the end of a four-year journey for the startup, but it believes it has landed in a good home with FireEye. “We are proud to announce that after many months of discussion, we are becoming part of the Mandiant Solutions portfolio, a solution organization inside FireEye,” Armistead wrote.

While FireEye was at it, it also announced a $400 million investment from Blackstone Tactical Opportunities fund and ClearSky (an investor in Respond), giving the public company a new influx of cash to make additional moves like the acquisition it made today.

It didn’t come cheap. “Under the terms of its investment, Blackstone and ClearSky will purchase $400 million in shares of a newly designated 4.5% Series A Convertible Preferred Stock of FireEye (the ‘Series A Preferred’), with a purchase price of $1,000 per share. The Series A Preferred will be convertible into shares of FireEye’s common stock at a conversion price of $18.00 per share,” the company explained in a statement. The stock closed at $14.24 today.

Respond, which was founded in 2016, raised $32 million, including a $12 million Series A in 2017 led by CRV and Foundation Capital and a $20 million Series B led by ClearSky last year, according to Crunchbase data.

Nov
18
2020
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Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures  told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 

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