DataRobot
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DataRobot CEO Dan Wright coming to TC Sessions: SaaS to discuss role of data in machine learning
Just about every company is sitting on vast amounts of data, which they can use to their advantage if they can just learn how to harness it. Data is actually the fuel for machine learning models, and with the proper tools, businesses can learn to process this data and build models to help them compete in a rapidly changing marketplace, to react more quickly to shifting customer requirements and to find insights faster than any human ever possibly could.
Boston-based DataRobot, a late-stage startup that has built a platform to help companies navigate the machine learning model lifecycle, has been raising money by the bushel over the last several years, including $206 million in September 2019 and another $300 million in July. DataRobot CEO Dan Wright will be joining us on a panel to discuss the role of data in business at TC Sessions: SaaS on October 27th.
The company covers the gamut of the machine learning lifecycle, including preparing data, operationalizing it and finally building APIs to make it useful for the organization as it attempts to build a soup-to-nuts platform. DataRobot’s broad platform approach has appealed to investors.
As we wrote at the time of the $206 million round:
The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.
DataRobot has raised a total of $1 billion on $6.3 billion post valuation, according to PitchBook data, and it’s been putting that money to work to add to its platform of services. Most recently the company acquired Algorithmia, which helps manage machine learning models.
As the pandemic has pushed more business online, companies are always looking for an edge, and one way to achieve that is by taking advantage of AI and machine learning. Wright will be joined on the data panel by Monte Carlo co-founder and CEO Barr Moses and AgentSync co-founder and CTO Jenn Knight to discuss the growing role of data in business operations
In addition to our discussion with Wright, the conference will also include Microsoft’s Jared Spataro, Amplitude’s Olivia Rose, as well as investors Kobie Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.
Buy your pass now to save up to $100. We can’t wait to see you in October!
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Announcing the agenda for TechCrunch Sessions: SaaS
On October 27, we’re taking on the ferociously competitive field of software as a service (SaaS), and we’re thrilled to announce our packed agenda, overflowing with some of the biggest names and most exciting startups in the industry. And you’re in luck, because $75 early-bird tickets are still on sale — make sure you book yours so you can enjoy all the agenda has to offer and save $100 bucks before prices go up!
Throughout the day, you can expect to hear from industry experts, and take part in discussions about the potential of new advances in data, open source, how to deal with the onslaught of security threats, investing in early-stage startups and plenty more.
We’ll be joined by some of the biggest names and the smartest and most prescient people in the industry, including Javier Soltero at Google, Kathy Baxter at Salesforce, Jared Spataro at Microsoft, Jay Kreps at Confluent, Sarah Guo at Greylock and Daniel Dines at UiPath.
You’ll be able to find and engage with people from all around the world through world-class networking on our virtual platform — all for $75 and under for a limited time, with even deeper discounts for nonprofits and government agencies, students and up-and-coming founders!
Our agenda showcases some of the powerhouses in the space, but also plenty of smaller teams that are building and debunking fundamental technologies in the industry. We still have a few tricks up our sleeves and will be adding some new names to the agenda over the next month, so keep your eyes open.
In the meantime, check out these agenda highlights:
Survival of the Fittest: Investing in Today’s SaaS Market
with Casey Aylward (Costanoa Ventures), Kobie Fuller (Upfront) and Sarah Guo (Greylock)
- The venture capital world is faster and more competitive than ever. For investors hoping to get into the hottest SaaS deal, things are even crazier. With more nontraditional money pouring into the sector, remote dealmaking now the norm and an increasingly global market for software startups, venture capitalists are being forced to shake up their own operations — and expectations. TechCrunch sits down with three leading investors to discuss how they are fighting for allocation in hot deals, what they’ve changed in their own processes, and what today’s best founders are demanding.
Data, Data Everywhere
with Ali Ghodsi (Databricks)
- As companies struggle to manage and share increasingly large amounts of data, it’s no wonder that Databricks, whose primary product is a data lake, was valued at a whopping $28 billion for its most recent funding round. We’re going to talk to CEO Ali Ghodsi about why his startup is so hot, and what comes next.
SaaS Security, Today and Tomorrow
with Edna Conway (Microsoft), Olivia Rose (Amplitude)
- Enterprises face a constant stream of threats, from nation states to cybercriminals and corporate insiders. After a year where billions worked from home and the cloud reigned supreme, startups and corporations alike can’t afford to stay off the security pulse. Find out what SaaS startups need to know about security now, and in the future.
Automation’s Moment Is Now
with Daniel Dines (UiPath), Laela Sturdy (CapitalG) and Dave Wright (ServiceNow)
- One thing we learned during the pandemic is the importance of automation, and that’s only likely to be more pronounced as we move forward. We’ll be talking to UiPath CEO Daniel Dines, Laela Sturdy, an investor at CapitalG and Dave Wright from ServiceNow about why this is automation’s moment.
Was the Pandemic Cloud Productivity’s Spark
with Javier Soltero (Google)
- One big aspect of SaaS is productivity apps like Gmail, Google Calendar and Google Drive. We’ll talk with executive Javier Soltero about the role Google Workspace plays in the Google cloud strategy.
The Future Is Wide Open
with Abby Kearns (Puppet), Aghi Marietti (Kong) and Jason Warner (Redpoint)
- Many startups today have an open-source component, and it’s no wonder. It builds an audience and helps drive sales. We’ll talk with Abby Kearns from Puppet, Augusto “Aghi” Marietti from Kong and Jason Warner, an investor at Redpoint, about why open source is such a popular way to build a business.
How Microsoft Shifted from On-Prem to the Cloud
with Jared Spataro (Microsoft)
- Jared Spataro has been with Microsoft for over 15 years and he was a part of the shift from strictly on-prem software to that which is dominated by the cloud. Today he runs one of the most successful SaaS products out there, and we’ll talk to him about how Microsoft made that shift and what it’s meant to the company.
How Startups are Turning Data into Software Gold
with Jenn Knight (Agentsync), Barr Moses (Monte Carlo) and Dan Wright (DataRobot)
- The era of big data is behind us. Today’s leading SaaS startups are working with data, instead of merely fighting to help customers collect information. We’ve collected three leaders from three data-focused startups that are forging new markets to get their insight on how today’s SaaS companies are leveraging data to build new companies, attack new problems and, of course, scale like mad.
What Happens After Your Startup Is Acquired
with Jyoti Bansal (Harness), Nick Mehta (GainSight) and Jewel Burkes Solomon (Partpic)
- We’ll speak to three founders about the emotional upheaval of being acquired and what happens after the check clears and the sale closes. Our panel includes Jyoti Bansal who founded AppDynamics, Jewel Burkes Solomon, who founded Partpic and Nick Mehta from GainSight.
Fireside Chat
with Jay Kreps (Confluent)
- Confluent, the streaming platform built on top of Apache Kafka, was born out of a project at LinkedIn, and rode that from startup to IPO. We’ll speak to co-founder and CEO Jay Kreps to learn about what that journey was like.
We’ll have more sessions and names shortly, so stay tuned. But get excited in the meantime, we certainly are.
Pro tip: Keep your finger on the pulse of TC Sessions: SaaS. Get updates when we announce new speakers, add events and offer ticket discounts.
Why should you carve a day out of your hectic schedule to attend TC Sessions: SaaS? This may be the first year we’ve focused on SaaS, but this ain’t our first rodeo. Here’s what other attendees have to say about their TC Sessions experience.
“TC Sessions: Mobility offers several big benefits. First, networking opportunities that result in concrete partnerships. Second, the chance to learn the latest trends and how mobility will evolve. Third, the opportunity for unknown startups to connect with other mobility companies and build brand awareness.” — Karin Maake, senior director of communications at FlashParking.
“People want to be around what’s interesting and learn what trends and issues they need to pay attention to. Even large companies like GM and Ford were there, because they’re starting to see the trend move toward mobility. They want to learn from the experts, and TC Sessions: Mobility has all the experts.” — Melika Jahangiri, vice president at Wunder Mobility.
TC Sessions: SaaS 2021 takes place on October 27. Grab your team, join your community and create opportunity. Don’t wait — jump on the early bird ticket sale right now.
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DataRobot expands platform and announces Zepl acquisition
DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and nontechnical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.
Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements, from those who have little data science understanding to those who can do their own machine learning coding in Python and R.
“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.
To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first-class environment for people who want to code,” he said.
Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform. The company plans to incorporate the Zepl functionality into the platform, while also leaving the standalone product in place.
Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.
The company is also introducing a no-code AI app builder that enables nontechnical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data on which the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.
Finally, the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model-building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.
DataRobot is based in Boston and was founded in 2012. It has raised more than $750 million and has a valuation of over $2.8 billion, according to PitchBook.
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Enterprise companies find MLOps critical for reliability and performance
Rish Joshi
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Contributor
Rish is an entrepreneur and investor. Previously, he was a VC at Gradient Ventures (Google’s AI fund), co-founded a fintech startup building an analytics platform for SEC filings and worked on deep-learning research as a graduate student in computer science at MIT.
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Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.
What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.
That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.
Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.
Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.
As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.
We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.
The rise of MLOps
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DataRobot is acquiring Paxata to add data prep to machine learning platform
DataRobot, a company best known for creating automated machine learning models known as AutoML, announced today that it intends to acquire Paxata, a data prep platform startup. The companies did not reveal the purchase price.
Paxata raised a total of $90 million before today’s acquisition, according to the company.
Up until now, DataRobot has concentrated mostly on the machine learning and data science aspect of the workflow — building and testing the model, then putting it into production. The data prep was left to other vendors like Paxata, but DataRobot, which raised $206 million in September, saw an opportunity to fill in a gap in their platform with Paxata.
“We’ve identified, because we’ve been focused on machine learning for so long, a number of key data prep capabilities that are required for machine learning to be successful. And so we see an opportunity to really build out a unique and compelling data prep for machine learning offering that’s powered by the Paxata product, but takes the knowledge and understanding and the integration with the machine learning platform from DataRobot,” Phil Gurbacki, SVP of product development and customer experience at DataRobot, told TechCrunch.
Prakash Nanduri, CEO and co-founder at Paxata, says the two companies were a great fit and it made a lot of sense to come together. “DataRobot has got a significant number of customers, and every one of their customers have a data and information management problem. For us, the deal allows us to rapidly increase the number of customers that are able to go from data to value. By coming together, the value to the customer is increased at an exponential level,” he explained.
DataRobot is based in Boston, while Paxata is in Redwood City, Calif. The plan moving forward is to make Paxata a west coast office, and all of the company’s almost 100 employees will become part of DataRobot when the deal closes.
While the two companies are working together to integrate Paxata more fully into the DataRobot platform, the companies also plan to let Paxata continue to exist as a standalone product.
DataRobot has raised more than $431 million, according to PitchBook data. It raised $206 million of that in its last round. At the time, the company indicated it would be looking for acquisition opportunities when it made sense.
This match-up seems particularly good, given how well the two companies’ capabilities complement one another, and how much customer overlap they have. The deal is expected to close before the end of the year.
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Boston-based DataRobot raises $206M Series E to bring AI to enterprise
Artificial intelligence is playing an increasingly large role in enterprise software, and Boston’s DataRobot has been helping companies build, manage and deploy machine learning models for some time now. Today, the company announced a $206 million Series E investment led by Sapphire Ventures.
Other participants in this round included new investors Tiger Global Management, World Innovation Lab, Alliance Bernstein PCI and EDBI, along with existing investors DFJ Growth, Geodesic Capital, Intel Capital, Sands Capital, NEA and Meritech.
Today’s investment brings the total raised to $431 million, according to the company. It has a pre-money valuation of $1 billion, according to PitchBook. DataRobot would not confirm this number.
The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.
The late-stage startup plans to use the money to continue building out its product line, while looking for acquisition opportunities where it makes sense. The company also announced the availability of a new product today, DataRobot MLOps, a tool to manage, monitor and deploy machine learning models across a large organization.
The company, which was founded in 2012, claims it has had triple-digit recurring revenue growth dating back to 2015, as well as one billion models built on the platform to date. Customers contributing to that number include a broad range of companies, such as Humana, United Airlines, Harvard Business School and Deloitte.
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