machine learning
Auto Added by WPeMatico
This Y Combinator startup is taking lab-grown meat upscale with elk, lamb and Wagyu beef cell lines
Last week a select group of 20 employees and guests gathered at an event space on the San Francisco Bay, and, while looking out at the Bay Bridge, dined on a selection of choice elk sausages, Wagyu meatloaf and lamb burgers — all of which were grown from a petri dish.
The dinner was a coming out party for Orbillion Bio, a new startup pitching today in Y Combinator’s latest demo day, that’s looking to take lab-grown meats from the supermarket to high-end, bespoke butcher shops.
Instead of focusing on pork, chicken and beef, Orbillion is going after so-called heritage meats — the aforementioned elk, lamb and Wagyu beef to start.
By focusing on more expensive-end products, Orbillion doesn’t have as much pressure to slash costs as dramatically as other companies in the cellular meat market, the thinking goes.
But there’s more to the technology than its bougie beef, elite elk and luscious lamb meat.
“Orbillion uses a unique accelerated development process producing thousands of tiny tissue samples, constantly iterating to find the best tissue and media combinations,” according to Holly Jacobus, whose firm, Joyance Partners, is an early investor in Orbillion. “This is much less expensive and more efficient than traditional methods and will enable them to respond quickly to the impressive demand they’re already experiencing.”
The company runs its multiple cell lines through a system of small bioreactors. Orbillion couples that with a high throughput screening and machine learning software system to build out a database of optimized tissue and media combinations. “The key to making lab grown meat work scalably is choosing the right cells cultured in the most efficient way possible,” Jacobus wrote.
Orbillion is co-founded by a deeply technical and highly experienced team of executives that’s led by Patricia Bubner, a former researcher at the German pharmaceutical giant Boehringer Ingelheim. Joining Bubner is Gabriel Levesque-Tremblay, a former director of the American Institute of Chemical Engineers, who was a post-doc at Berkeley with Bubner and serves as the company’s chief technology officer. Rounding out the senior leadership is Samet Yildirim, the chief operating officer and a veteran executive of Boehringer Ingelheim (he actually served as Bubner’s boss).
Orbillion Bio co-founders Gabriel Levesque-Tremblay, CTO; Patricia Bubner, CEO; and Samet Yildirim, COO. Image Credit: Orbillion Bio
For Bubner, the focus on heritage meats is as much a function of her background growing up in rural Austria as it is about economics. A longtime, self-described foodie and a nerd, Bubner went into chemistry because she ultimately wanted to apply science to the food business. And she wants Orbillion to make not just meat, but the most delicious meats.
It’s an aim that fits with how many other companies have approached the market when they’re looking to commercialize a novel technology. Higher-end products, or products with unique flavor profiles that are unique to the production technologies available, are more likely to be commercially viable sooner than those competing with commodity products. Why focus on angus beef when you can focus on a much more delicious breed of animal?
For Bubner, it’s not just about making a pork replacement, it’s about making the tastiest pork replacement.
“I’m just fascinated and can see the future in us being able to further change the way we produce food to be more efficient,” she said. “We’re at this inflection point. I’m a nerd, I’m a foodie, and I really wanted to use my skills to make a change. I wanted to be part of that group of people that can really have an impact on the way we eat. For me there’s no doubt that a large percentage of our food will be from alternative proteins — plant based, fermentation and lab-grown meat.”
Joining Boehringer Ingelheim was a way for Bubner to become grounded in the world of big bioprocessing. It was preparation for her foray into lab-grown meat, she said.
“We are a product company. Our goal is to make the most flavorful steaks. Our first product will not be whole cuts of steak. The first product is going to be a Wagyu beef product that we plan on putting out in 2023,” Bubner said. “It’s a product that’s going to be based on more of a minced product. Think Wagyu sashimi.”
To get to market, Bubner sees the need not just for a new approach to cultivating choice meats, but a new way of growing other inputs as well, from the tissue scaffolding needed to make larger cuts that resemble traditional cuts of meat, or the fats that will need to be combined with the meat cells to give flavor.
That means there are still opportunities for companies like Future Fields, Matrix Meats and Turtle Tree Scientific to provide inputs that are integrated into the final, branded product.
Bubner’s also thinking about the supply chain beyond her immediate potential partners in the manufacturing process. “Part of my family were farmers and construction workers and the others were civil engineers and architects. I hold farmers in high respect… and think the people who grow the food and breed the animals don’t get recognition for the work that they do.”
She envisions working in concert with farmers and breeders in a kind of licensing arrangement, potentially, where the owners of the animals that produce the cell lines can share in the rewards of their popularization and wider commercial production.
That also helps in the mission of curbing the emissions associated with big agribusiness and breeding and raising livestock on a massive scale. If you only need a few animals to make the meat, you don’t have the same environmental footprint for the farms.
“We need to make sure that we don’t make the mistakes that we did in the past that we only breed animals for yield and not for flavor,” said Bubner.
Even though the company is still in its earliest days, it already has one letter of intent, with one of San Francisco’s most famous butchers. Guy Crims, also known as “Guy the Butcher,” has signed a letter of intent to stock Orbillion Bio’s lab-grown Wagyu in his butcher shop, Bubner said. “He’s very much a proponent of lab-grown meat.”
Now that the company has its initial technology proven, Orbillion is looking to scale rapidly. It will take roughly $3.5 million for the company to get a pilot plant up and running by the end of 2022, and that’s in addition to the small $1.4 million seed round the company has raised from Joyant and firms like VentureSouq.
“The way I see an integrated model working later on is to have the farmers be the breeders of animals for cultivated meat. That can reduce the number of cows on the planet to a couple of hundred thousand,” Bubner said of her ultimate goal. “There’s a lot of talking about if you do lab-grown meat you want to put me out of business. It’s not like we’re going to abolish animal agriculture tomorrow.”
Image Credit: Getty Images
Powered by WPeMatico
OctoML raises $28M Series B for its machine learning acceleration platform
OctoML, a Seattle-based startup that offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project, today announced that it has raised a $28 million Series B funding round led by Addition. Previous investors Madrona Venture Group and Amplify Partners also participated in this round, which brings the company’s total funding to $47 million. The company last raised in April 2020, when it announced its $15 million Series A round led by Amplify.
The promise of OctoML, which was founded by the team that also created TVM, is that developers can bring their models to its platform and the service will automatically optimize that model’s performance for any given cloud or edge device.
As Brazil-born OctoML co-founder and CEO Luis Ceze told me, since raising its Series A round, the company started onboarding some early adopters to its “Octomizer” SaaS platform.
Image Credits: OctoML
“It’s still in early access, but we are we have close to 1,000 early access sign-ups on the waitlist,” Ceze said. “That was a pretty strong signal for us to end up taking this [funding]. The Series B was pre-emptive. We were planning on starting to raise money right about now. We had barely started spending our Series A money — we still had a lot of that left. But since we saw this growth and we had more paying customers than we anticipated, there were a lot of signals like, ‘hey, now we can accelerate the go-to-market machinery, build a customer success team and continue expanding the engineering team to build new features.’ ”
Ceze tells me that the team also saw strong growth signals in the overall community around the TVM project (with about 1,000 people attending its virtual conference last year). As for its customer base (and companies on its waitlist), Ceze says it represents a wide range of verticals that range from defense contractors to financial services and life science companies, automotive firms and startups in a variety of fields.
Recently, OctoML also launched support for the Apple M1 chip — and saw very good performance from that.
The company has also formed partnerships with industry heavyweights like Microsoft (which is also a customer), Qualcomm and AMD to build out the open-source components and optimize its service for an even wider range of models (and larger ones, too).
On the engineering side, Ceze tells me that the team is looking at not just optimizing and tuning models but also the training process. Training ML models can quickly become costly and any service that can speed up that process leads to direct savings for its users — which in turn makes OctoML an easier sell. The plan here, Ceze tells me, is to offer an end-to-end solution where people can optimize their ML training and the resulting models and then push their models out to their preferred platform. Right now, its users still have to take the artifact that the Octomizer creates and deploy that themselves, but deployment support is on OctoML’s roadmap.
“When we first met Luis and the OctoML team, we knew they were poised to transform the way ML teams deploy their machine learning models,” said Lee Fixel, founder of Addition. “They have the vision, the talent and the technology to drive ML transformation across every major enterprise. They launched Octomizer six months ago and it’s already becoming the go-to solution developers and data scientists use to maximize ML model performance. We look forward to supporting the company’s continued growth.”
Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE at checkout to get 20% off tickets right here.
Powered by WPeMatico
New Relic expands its AIOps services
In recent years, the publicly traded observability service New Relic started adding more machine learning-based tools to its platform for AI-assisted incident response when things don’t quite go as planned. Today, it is expanding this feature set with the launch of a number of new capabilities for what it calls its “New Relic Applied Intelligence Service.”
This expansion includes an anomaly detection service that is even available for free users, the ability to group alerts from multiple tools when the models think it’s a single issue that is triggering all of these alerts and new ML-based root cause analysis to help eliminate some of the guesswork when problems occur. Also new (and in public beta) is New Relic’s ability to detect patterns and outliers in log data that is stored in the company’s data platform.
The main idea here, New Relic’s director of product marketing Michael Olson told me, is to make it easier for companies of all sizes to reap the benefits of AI-enhanced ops.
Image Credits: New Relic
“It’s been about a year since we introduced our first set of AIops capabilities with New Relic Applied Intelligence to the market,” he said. “During that time, we’ve seen significant growth in adoption of AIops capabilities through New Relic. But one of the things that we’ve heard from organizations that have yet to foray into adopting AIops capabilities as part of their incident response practice is that they often find that things like steep learning curves and long implementation and training times — and sometimes lack of confidence, or knowledge of AI and machine learning — often stand in the way.”
The new platform should be able to detect emerging problems in real time — without the team having to pre-configure alerts. And when it does so, it’ll smartly group all of the alerts from New Relic and other tools together to cut down on the alert noise and let engineers focus on the incident.
“Instead of an alert storm when a problem occurs across multiple tools, engineers get one actionable issue with alerts automatically grouped based on things like time and frequency, based on the context that they can read in the alert messages. And then now with this launch, we’re also able to look at relationship data across your systems to intelligently group and correlate alerts,” Olson explained.
Image Credits: New Relic
Maybe the highlight for the ops teams that will use these new features, though, is New Relic’s ability to pinpoint the probable root cause of a problem. As Guy Fighel, the general manager of applied intelligence and vice president of product engineering at New Relic, told me, the idea here is not to replace humans but to augment teams.
“We provide a non-black-box experience for teams to craft the decisions and correlation and logic based on their own knowledge and infuse the system with their own knowledge,” Fighel noted. “So you can get very specific based on your environment and needs. And so because of that and because we see a lot of data coming from different tools — all going into New Relic One as the data platform — our probable root cause is very accurate. Having said that, it is still a probable root cause. So although we are opinionated about it, we will never tell you, ‘hey, go fix that, because we’re 100% sure that’s the case.’ You’re the human, you’re in control.”
The AI system also asks users for feedback, so that the model gets refined with every new incident, too.
Fighel tells me that New Relic’s tools rely on a variety of statistical analysis methods and machine learning models. Some of those are unique to individual users while others are used across the company’s user base. He also stressed that all of the engineers who worked on this project have a background in site reliability engineering — so they are intimately familiar with the problems in this space.
With today’s launch, New Relic is also adding a new integration with PagerDuty and other incident management tools so that the state of a given issue can be synchronized bi-directionally between them.
“We want to meet our customers where they are and really be data source agnostic and enable customers to pull in data from any source, where we can then enrich that data, reduce noise and ultimately help our customers solve problems faster,” said Olson.
Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE at checkout to get 20% off tickets right here.
Powered by WPeMatico
A crypto company’s journey to Data 3.0
Data is a gold mine for a company.
If managed well, it provides the clarity and insights that lead to better decision-making at scale, in addition to an important tool to hold everyone accountable.
However, most companies are stuck in Data 1.0, which means they are leveraging data as a manual and reactive service. Some have started moving to Data 2.0, which employs simple automation to improve team productivity. The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation. This is our journey to Data 3.0.
The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation.
Coinbase is neither a finance company nor a tech company — it’s a crypto company. This distinction has big implications for how we work with data. As a crypto company, we work with three major types of data (instead of the usual one or two types of data), each of which is complex and varied:
- Blockchain: decentralized and publicly available.
- Product: large and real-time.
- Financial: high-precision and subject to many financial/legal/compliance regulations.
Image Credits: Michael Li/Coinbase
Our focus has been on how we can scale value creation by making this varied data work together, eliminating data silos, solving issues before they start and creating opportunities for Coinbase that wouldn’t exist otherwise.
Having worked at tech companies like LinkedIn and eBay, and also those in the finance sector, including Capital One, I’ve observed firsthand the evolution from Data 1.0 to Data 3.0. In Data 1.0, data is seen as a reactive function providing ad-hoc manual services or firefighting in urgent situations.
Powered by WPeMatico
Socure raises $100M at $1.3B valuation, proving identity verification is hotter than ever
The COVID-19 pandemic has accelerated digital adoption in a way that no one could have ever anticipated, and as more people conduct more services online and via mobile devices, businesses have had to work even harder to validate users and security. One company working to serve that need, Socure — which uses AI and machine learning to verify identities — announced Tuesday that it has raised $100 million in a Series D funding round at a $1.3 billion valuation.
Given how much of our lives have shifted online, it’s no surprise that the U.S. digital identity market is projected to increase to over $30 billion by 2023 from just under $15 billion in 2019, according to One World Identity. This has led to skyrocketing demand for the services provided by identity verification companies.
The founding team set out on a mission to be able to verify 100% of “good IDs” in real-time while “completely eliminating” identity fraud across the internet.
Historically, Socure has been focused on the financial services industry, but it plans to use its new capital to further expand into “every consumer-facing vertical” including online gaming, healthcare, telco, e-commerce and on-demand services.
The startup’s predictive analytics platform applies artificial intelligence and machine-learning techniques with online/offline data intelligence (from email, phone, address, IP, device, velocity and the broader internet) to verify that people are, in fact, who they say they are when applying for various accounts.
Today, Socure has more than 350 customers including three top five banks, six top 10 card issuers, a “top” credit bureau and over 75 fintechs such as Varo Money, Public, Chime and Stash.
In 2020, Socure grew its customer base by over 85% year over year and expanded its workforce by over 50% to about 240 people today.
Accel led Socure’s latest financing, which included participation from existing backers Commerce Ventures, Scale Venture Partners, Flint Capital, Citi Ventures, Wells Fargo Strategic Capital, Synchrony, Sorenson, Two Sigma Ventures and others.
The round comes less than six months after the company raised $35 million in a round led by Sorenson Ventures, and brings the New York-based company’s total raised to $196 million since its 2012 inception.
Socure founder and CEO Johnny Ayers says his company’s identity management products can help B2C enterprises achieve know-your-customer (KYC) auto-approval rates of up to 97%. This means that financial institutions can more easily capture fraud, for example, via Socure’s single API. The company also claims that by more easily verifying thin-file (those without much credit history) and young consumers, it can help reduce the underbanked population.
The pandemic and resulting shutdowns resulted in a massive demand for trusted digital identity, Ayers believes.
“This growth tracks with a larger trend marked by the broad migration of businesses to accept applications and onboard new customers online, with many companies accelerating their transformation from digital-first to digital-only,” he told TechCrunch.
Overall fraud attempts among Socure’s existing customer base nearly doubled in the second quarter of 2020 — with certain segments seeing rises as high as 150%, according to Ayers.
“These instances did not involve actual fraud but instead were flagged by Socure as suspicious and blocked prior to inflicting damage,” he said.
Looking ahead, the company plans to use its new capital to also enhance its product offering as it continues to develop patents.
Accel partner Amit Jhawar will join Socure’s board as part of the funding round.
In a blog post, Jhawar described Socure as “a purpose-built solution designed to handle the wave of new online users because its machine learning models have learned from every identity it has already seen.”
As former COO at Braintree and general manager at Venmo, Jhawar knows a thing or two about the importance of identity verification, especially in the financial services space.
He wrote: “I knew immediately that the Socure solution would be a game-changer because the solution can be used in every step of the customer lifecycle, from account creation to login to transaction.”
Socure also has hinted that it has an IPO in its future.
In a written statement, Ayers said: “We are incredibly grateful for the chance to innovate and partner to solve this problem with some of the greatest companies in the world and are energized for the opportunities that lay ahead for Socure, especially as we make our march to a potential IPO.”
Via email, he told TechCrunch that the company will “potentially” look at public markets in 2022 or 2023, when it feels “the time is right for the business.”
The story was updated post-publication with live comments from Socure
Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE at checkout to get 20% off tickets right here.
Powered by WPeMatico
Noogata raises $12M seed round for its no-code enterprise AI platform
Noogata, a startup that offers a no-code AI solution for enterprises, today announced that it has raised a $12 million seed round led by Team8, with participation from Skylake Capital. The company, which was founded in 2019 and counts Colgate and PepsiCo among its customers, currently focuses on e-commerce, retail and financial services, but it notes that it will use the new funding to power its product development and expand into new industries.
The company’s platform offers a collection of what are essentially pre-built AI building blocks that enterprises can then connect to third-party tools like their data warehouse, Salesforce, Stripe and other data sources. An e-commerce retailer could use this to optimize its pricing, for example, thanks to recommendations from the Noogata platform, while a brick-and-mortar retailer could use it to plan which assortment to allocate to a given location.
Image Credits: Noogata
“We believe data teams are at the epicenter of digital transformation and that to drive impact, they need to be able to unlock the value of data. They need access to relevant, continuous and explainable insights and predictions that are reliable and up-to-date,” said Noogata co-founder and CEO Assaf Egozi. “Noogata unlocks the value of data by providing contextual, business-focused blocks that integrate seamlessly into enterprise data environments to generate actionable insights, predictions and recommendations. This empowers users to go far beyond traditional business intelligence by leveraging AI in their self-serve analytics as well as in their data solutions.”
Image Credits: Noogata
We’ve obviously seen a plethora of startups in this space lately. The proliferation of data — and the advent of data warehousing — means that most businesses now have the fuel to create machine learning-based predictions. What’s often lacking, though, is the talent. There’s still a shortage of data scientists and developers who can build these models from scratch, so it’s no surprise that we’re seeing more startups that are creating no-code/low-code services in this space. The well-funded Abacus.ai, for example, targets about the same market as Noogata.
“Noogata is perfectly positioned to address the significant market need for a best-in-class, no-code data analytics platform to drive decision-making,” writes Team8 managing partner Yuval Shachar. “The innovative platform replaces the need for internal build, which is complex and costly, or the use of out-of-the-box vendor solutions which are limited. The company’s ability to unlock the value of data through AI is a game-changer. Add to that a stellar founding team, and there is no doubt in my mind that Noogata will be enormously successful.”
Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion. Use code “TCARTICLE at checkout to get 20% off tickets right here.
Powered by WPeMatico
DeepSee.ai raises $22.6M Series A for its AI-centric process automation platform
DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.
The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is “robotic process automation,” but DeepSee.ai argues that what it does is different. I describe its system as “knowledge process automation” (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but helps them leverage AI to turn into actionable insights the plethora of data that businesses now collect.
Image Credits: DeepSee.ai
“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation — delivering faster, better and cheaper results for our users.”
To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.
Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.
That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.
“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost and mitigate risk,” said Sean Cunningham, managing director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”
Powered by WPeMatico
Microsoft launches Azure Percept, its new hardware and software platform to bring AI to the edge
Microsoft today announced Azure Percept, its new hardware and software platform for bringing more of its Azure AI services to the edge. Percept combines Microsoft’s Azure cloud tools for managing devices and creating AI models with hardware from Microsoft’s device partners. The general idea here is to make it far easier for all kinds of businesses to build and implement AI for things like object detection, anomaly detections, shelf analytics and keyword spotting at the edge by providing them with an end-to-end solution that takes them from building AI models to deploying them on compatible hardware.
To kickstart this, Microsoft also today launches a hardware development kit with an intelligent camera for vision use cases (dubbed Azure Percept Vision). The kit features hardware-enabled AI modules for running models at the edge, but it can also be connected to the cloud. Users will also be able to trial their proofs-of-concept in the real world because the development kit conforms to the widely used 80/20 T-slot framing architecture.
In addition to Percept Vision, Microsoft is also launching Azure Percept Audio for audio-centric use cases.
Azure Percept devices, including Trust Platform Module, Azure Percept Vision and Azure Percept Audio. Image Credits: Microsoft
“We’ve started with the two most common AI workloads, vision and voice, sight and sound, and we’ve given out that blueprint so that manufacturers can take the basics of what we’ve started,” said Roanne Sones, the corporate vice president of Microsoft’s edge and platform group. “But they can envision it in any kind of responsible form factor to cover a pattern of the world.”
Percept customers will have access to Azure’s cognitive service and machine learning models and Percept devices will automatically connect to Azure’s IoT hub.
Microsoft says it is working with silicon and equipment manufacturers to build an ecosystem of “intelligent edge devices that are certified to run on the Azure Percept platform.” Over the course of the next few months, Microsoft plans to certify third-party devices for inclusion in this program, which will ideally allow its customers to take their proofs-of-concept and easily deploy them to any certified devices.
“Anybody who builds a prototype using one of our development kits, if they buy a certified device, they don’t have to do any additional work,” said Christa St. Pierre, a product manager in Microsoft’s Azure edge and platform group.
St. Pierre also noted that all of the components of the platform will have to conform to Microsoft’s responsible AI principles — and go through extensive security testing.
Early Stage is the premiere “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company-building: Fundraising, recruiting, sales, legal, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included in each for audience questions and discussion.
Powered by WPeMatico
DataJoy raises $6M seed to help SaaS companies track key business metrics
Every business needs to track fundamental financial information, but the data typically lives in a variety of silos, making it a constant challenge to understand a company’s overall financial health. DataJoy, an early-stage startup, wants to solve that issue. The company announced a $6 million seed round today led by Foundation Capital with help from Quarry VC, Partech Partners, IGSB, Bow Capital and SVB.
Like many startup founders, CEO Jon Lee has experienced the frustration firsthand of trying to gather this financial data, and he decided to start a company to deal with it once and for all. “The reason why I started this company was that I was really frustrated at Copper, my last company, because it was really hard just to find the answers to simple business questions in my data,” he told me.
These include basic questions like how the business is doing this quarter, if there are any surprises that could throw the company off track and where are the best places to invest in the business to accelerate more quickly.
The company has decided to concentrate its efforts for starters on SaaS companies and their requirements. “We basically focus on taking the work out of revenue intelligence, and just give you the insights that successful companies in the SaaS vertical depend on to be the largest and fastest growing in the market,” Lee explained.
The idea is to build a product with a way to connect to key business systems, pull the data and answer a very specific set of business questions, while using machine learning to provide more proactive advice.
While the company is still in the process of building the product and is pre-revenue, it has begun developing the pieces to ultimately help companies answer these questions. Eventually it will have a set of connectors to various key systems like Salesforce for CRM, HubSpot and Marketo for marketing, NetSuite for ERP, Gainsight for customer experience and Amplitude for product intelligence.
Lee says the set of connectors will be as specific as the questions themselves and based on their research with potential customers and what they are using to track this information. Ashu Garg, general partner at lead investor Foundation Capital, says that he was attracted to the founding team’s experience, but also to the fact they were solving a problem he sees all the time sitting on the boards of various SaaS startups.
“I spend my life in the board meetings. It’s what I do, and every CEO, every board is looking for straight answers for what should be obvious questions, but they require this intersection of data,” Garg said. He says to an extent, it’s only possible now due to the evolution of technology to pull this all together in a way that simplifies this process.
The company currently has 11 employees, with plans to double that by the middle of this year. As a longtime entrepreneur, Lee says that he has found that building a diverse workforce is essential to building a successful company. “People have found diversity usually [results in a company that is] more productive, more creative and works faster,” Lee said. He said that that’s why it’s important to focus on diversity from the earliest days of the company, while being proactive to make that happen. For example, ensuring you have a diverse set of candidates to choose from when you are reviewing resumes.
For now, the company is 100% remote. In fact, Lee and his co-founder, Chief Product Officer Ken Wong, who previously ran AI and machine learning at Tableau, have yet to meet in person, but they are hoping that changes soon. The company will eventually have a presence in Vancouver and San Mateo whenever offices start to open.
Powered by WPeMatico
Aquarium scores $2.6M seed to refine machine learning model data
Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors, including Cruise co-founders Kyle Vogt and Dan Kan.
When the two co-founders, CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.
“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.
He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.
“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.
The idea is to get a model into production that outperforms humans. One customer, Sterblue, offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.
The Aquarium team. Image: Aquarium
Aquarium currently has seven employees, including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.
The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.
Powered by WPeMatico