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Hex lands $5.5M seed to help data scientists share data across the company
As companies embrace the use of data, hiring more data scientists, a roadblock persists around sharing that data. It requires too much copying and pasting and manual work. Hex, a new startup, wants to change that by providing a way to dispense data across the company in a streamlined and elegant way.
Today, the company announced a $5.5 million seed investment, and also announced that it’s opening up the product from a limited beta to be more widely available. The round was led by Amplify Partners, with help from Box Group, XYZ, Data Community Fund, Operator Collective and a variety of individual investors. The company closed the round last July, but is announcing it for the first time today.
Co-founder and CEO Barry McCardel says that it’s clear that companies are becoming more data-driven and hiring data scientists and analysts at a rapid pace, but there is an issue around data sharing, one that he and his co-founders experienced firsthand when they were working at Palantir.
They decided to develop a purpose-built tool for sharing data with other parts of the organization that are less analytically technical than the data science team working with these data sets. “What we do is we make it very easy for data scientists to connect to their data, analyze and explore it in notebooks. […] And then they can share their work as interactive data apps that anyone else can use,” McCardel explained.
Most data scientists work with their data in online notebooks like Jupyter, where they can build SQL queries and enter Python code to organize it, chart it and so forth. What Hex is doing is creating this super-charged notebook that lets you pull a data set from Snowflake or Amazon Redshift, work with and format the data in an easy way, then drag and drop components from the notebook page — maybe a chart or a data set — and very quickly build a kind of app that you can share with others.
Image Credits: Hex
The startup has nine employees, including co-founders McCardel, CTO Caitlin Colgrove and VP of architecture Glen Takahashi. “We’ve really focused on the team front from an early stage, making sure that we’re building a diverse team. And actually today our engineering team is majority female, which is definitely the first time that that’s ever happened to me,” Colgrove said.
She is also part of a small percentage of female founders. A report last year from Silicon Valley Bank found that while the number was heading in the right direction, only 28% of U.S. startups have at least one female founder. That was up from 22% in 2017.
The company was founded in late 2019 and the founders spent a good part of last year building the product and working with design partners. They have a small set of paying customers, and are looking to expand that starting today. While customers still need to work with the Hex team for now to get going, the plan is to make the product self-serve some time later this year.
Hex’s early customers include Glossier, imgur and Pave.
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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.”
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Fishtown Analytics raises $29.5M Series B for its data engineering platform
Fishtown Analytics, the Philadelphia-based company behind the dbt open-source data engineering tool, today announced that it has raised a $29.5 million Series B round led by Sequoia Capital, with participation from previous investors Andreessen Horowitz and Amplify Partners.
The company is building a platform that allows data analysts to more easily create and disseminate organizational knowledge. Its focus is on data modeling, with its dbt tool allowing anybody who knows SQL to build data transformation workflows. Dbt also features support for automatically testing data quality and documenting changes, but maybe most importantly it uses standard software engineering techniques to help engineers collaborate on code and integrate changes continuously.
If this all sounds a bit familiar, it’s probably because you saw that Fishtown Analytics also announced a $12.9 million Series A round in April. It’s not often we see both a Series A and B round within half a year, but that goes to show how the market for Fishtown’s service is expanding as companies continue to grapple with how to best make use of their data — and how much investors want to be part of that.
Image Credits: Fishtown
“This was a very productive thing for us,” Fishtown Analytics co-founder and CEO Tristan Handy told me when I asked him why he raised again so quickly. “It’s standard best practice to do quarterly catch-ups with investors and eventually you’ll be ready to fundraise. And Matt Miller from Sequoia showed up to one of these quarterly catch-ups and he shared the 40-page memo that he had written to the Sequoia partnership — and he came with the term sheet.”
Initially, Handy declined. “We’re very bullheaded people, I think, as many founders are. It took some real reflection and thinking about, ‘is this what we want to be doing right now?’ ”
In the end, though, the team decided to go ahead with this round — mostly because this round allowed the team to think long-term and provided stability and certainty.
One thing Handy has always been very clear about is that he did not found Fishtown to purely build the largest possible company but to solve its users’ problems, even as the market looked at companies like Databricks and Snowflake — and their financial success — as potential analogs. “My worry was that the financial markets were driving things that weren’t necessarily going to be good for our users,” Handy said.
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Temporal raises $18.75M for its microservices orchestration platform
Temporal, a Seattle-based startup that is building an open-source, stateful microservices orchestration platform, today announced that it has raised an $18.75 million Series A round led by Sequoia Capital. Existing investors Addition Ventures and Amplify Partners also joined, together with new investor Madrona Venture Group. With this, the company has now raised a total of $25.5 million.
Founded by Maxim Fateev (CEO) and Samar Abbas (CTO), who created the open-source Cadence orchestration engine during their time at Uber, Temporal aims to make it easier for developers and operators to run microservices in production. Current users include the likes of Box and Snap.
“Before microservices, coding applications was much simpler,” Temporal’s Fateev told me. “Resources were always located in the same place — the monolith server with a single DB — which meant developers didn’t have to codify a bunch of guessing about where things were. Microservices, on the other hand, are highly distributed, which means developers need to coordinate changes across a number of servers in different physical locations.”
Those servers could go down at any time, so engineers often spend a lot of time building custom reliability code to make calls to these services. As Fateev argues, that’s table stakes and doesn’t help these developers create something that builds real business value. Temporal gives these developers access to a set of what the team calls “reliability primitives” that handle these use cases. “This means developers spend far more time writing differentiated code for their business and end up with a more reliable application than they could have built themselves,” said Fateev.
Temporal’s target use is virtually any developer who works with microservices — and wants them to be reliable. Because of this, the company’s tool — despite offering a read-only web-based user interface for administering and monitoring the system — isn’t the main focus here. The company also doesn’t have any plans to create a no-code/low-code workflow builder, Fateev tells me. However, since it is open-source, quite a few Temporal users build their own solutions on top of it.
The company itself plans to offer a cloud-based Temporal-as-a-Service offering soon. Interestingly, Fateev tells me that the team isn’t looking at offering enterprise support or licensing in the near future. “After spending a lot of time thinking it over, we decided a hosted offering was best for the open-source community and long-term growth of the business,” he said.
Unsurprisingly, the company plans to use the new funding to improve its existing tool and build out this cloud service, with plans to launch it into general availability next year. At the same time, the team plans to say true to its open-source roots and host events and provide more resources to its community.
“Temporal enables Snapchat to focus on building the business logic of a robust asynchronous API system without requiring a complex state management infrastructure,” said Steven Sun, Snap Tech Lead, Staff Software Engineer. “This has improved the efficiency of launching our services for the Snapchat community.”
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Anyscale, from the creators of the Ray distributed computing project, launches with $20.6M led by a16z
Open source has become a critical building block of modern software, and today a new startup is coming out of stealth to capitalise on one of the newer frontiers in open source: using it to build and manage distributed application environments, an approach being used increasingly to handle large computing projects, such as those involving artificial intelligence or scientific or other complex calculations.
Anyscale, a startup founded by the same team that built the Project Ray open-source distributed programming framework out of UC Berkeley — Robert Nishihara, Philipp Moritz and Ion Stoica, and Berkeley professor Michael I. Jordan — has raised $20.6 million in a Series A round of funding led by Andreessen Horowitz, with participation also from NEA, Intel Capital, Ant Financial, Amplify Partners, 11.2 Capital and The House Fund.
The company plans to use the money to build out its first commercial products — details of which are still being kept under wraps but will more generally include the ability to easily scale out a computing project from one laptop to a cluster of machines; and a group of libraries and applications to manage projects. These are expected to launch next year.
“Right now we are focused on making Ray a standard for building applications,” said Stoica in an interview. “The company will build tools and a runtime platform for Ray. So, if you want to run a Ray application securely and with high performance then you will use our product.”
The funding is partly strategic: Intel is one of the big companies that has been using Ray for its own computing projects, alongside Amazon, Microsoft and Ant Financial.
“Intel IT has been leveraging Ray to scale Python workloads with minimal code modifications,” said Moty Fania, principal engineer and chief technology officer for Intel IT’s Enterprise and Platform Group, in a statement. “With the implementation into Intel’s manufacturing and testing processes, we have found that Ray helps increase the speed and scale of our hyperparameter selection techniques and auto modeling processes used for creating personalized chip tests. For us, this has resulted in reduced costs, additional capacity and improved quality.”
With an impressive user list like this for the free-to-use Ray, you might ask yourself, what is the purpose of Anyscale? As Stoica and Nishihara explained, the idea will be to create simpler and easier ways to implement Ray, to make it usable whether you’re one of the Amazons of the world, or a more modest, and possibly less tech-centric operation.
“We see that this will be valuable mostly for companies who do not have engineering experts,” Stoica said.
The problem that Anyscale is solving is a central one to the future of large-scale, involved computing projects: there are an increasing array of problems that are being tackled with computing solutions, but as the complexity of the work involved increases, there is a limit to how much work a single machine (even a big one) can handle. (Indeed, Anyscale cites IDC figures estimating that the amount of data created and copied annually will reach 175 zettabytes by 2025.)
While one day there may be quantum-computing machines that can run efficiently and at scale to address these kinds of tasks, today this isn’t a realistic option, and so distributed computing has emerged as a solution.
Ray was devised as a standard to use to implement distributed computing environments, but on its own it’s too technical for the uninitiated to use.
“Imagine you’re a biologist,” added Nishihara. “You can write a simple program and run it at a large scale, but to do that successfully you need not only to be a biology expert but a computing expert. That’s just way too high a barrier.”
The people behind Anyscale (and Ray) have a long and very credible list of other work behind them that speaks to the opportunities that are being spotted here. Stoica, for example, was also the co-founder of Databricks, Conviva and one of the original developers of Apache Spark.
“I worked on Databricks with Ion and that’s how it started,” Andreessen Horowitz co-founder Ben Horowitz said in an interview. He added that the firm has been a regular investor into projects coming out of UC Berkeley. Ray, and more specifically Anyscale, is notable for its relevance to today’s computing needs.
“With Ray it was a very attractive project because of the open-source metrics but also because of the issue it addresses,” he said.
“We’ve been grappling with Moore’s Law being over, but more interestingly, it’s inadequate for things like artificial intelligence applications,” where increasing computing power is needed that outstrips what any single machine can do. “You have to be able to deal with distributed computing, but the problem for everyone but Google is that distributed computing is hard, so we have been looking for a solution.”
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Backtrace, a debugging startup led by former AppNexus engineers, raises $5M
Debugging startup Backtrace I/O was launched to solve a real problem that its founders faced when they were engineers at adtech company AppNexus — at least according to Backtrace CEO and co-founder Abel Mathew. Mathew told me Backtrace aims to “solve the process of debugging,” something that most companies tackle by “cobbling together very old, outdated solutions”… Read More
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