Personal blog of Yzmir Ramirez

Databricks this morning confirmed earlier reports that it was raising new capital at a higher valuation. The data- and AI-focused company has secured a $1.6 billion round at a $38 billion valuation, it said. Bloomberg first reported last week that Databricks was pursuing new capital at that price.

The Series H was led by Counterpoint Global, a Morgan Stanley fund. Other new investors included Baillie Gifford, UC Investments and ClearBridge. A grip of prior investors also kicked in cash to the round.

The new funding brings Databricks’ total private funding raised to $3.5 billion. Notably, its latest raise comes just seven months after the late-stage startup raised $1 billion on a $28 billion valuation. Its new valuation represents paper value creation in excess of $1 billion per month.

The company, which makes open source and commercial products for processing structured and unstructured data in one location, views its market as a new technology category. Databricks calls the technology a data “lakehouse,” a mashup of data lake and data warehouse.

Databricks CEO and co-founder Ali Ghodsi believes that its new capital will help his company secure market leadership.

For context, since the 1980s, large companies have stored massive amounts of structured data in data warehouses. More recently, companies like Snowflake and Databricks have provided a similar solution for unstructured data called a data lake.

In Ghodsi’s view, combining structured and unstructured data in a single place with the ability for customers to execute data science and business-intelligence work without moving the underlying data is a critical change in the larger data market.

“[Data lakehouses are] a new category, and we think there’s going to be lots of vendors in this data category. So it’s a land grab. We want to quickly race to build it and complete the picture,” he said in an interview with TechCrunch.

Ghodsi also pointed out that he is going up against well-capitalized competitors and that he wants the funds to compete hard with them.

“And you know, it’s not like we’re up against some tiny startups that are getting seed funding to build this. It’s all kinds of [large, established] vendors,” he said. That includes Snowflake, Amazon, Google and others who want to secure a piece of the new market category that Databricks sees emerging.

The company’s performance indicates that it’s onto something.

Growth

Databricks has reached the $600 million annual recurring revenue (ARR) milestone, it disclosed as part of its funding announcement. It closed 2020 at $425 million ARR, to better illustrate how quickly it is growing at scale.

Per the company, its new ARR figure represents 75% growth, measured on a year-over-year basis.

That’s quick for a company of its size; per the Bessemer Cloud Index, top-quartile public software companies are growing at around 44% year over year. Those companies are worth around 22x their forward revenues.

At its new valuation, Databricks is worth 63x its current ARR. So Databricks isn’t cheap, but at its current pace should be able to grow to a size that makes its most recent private valuation easily tenable when it does go public, provided that it doesn’t set a new, higher bar for its future performance by raising again before going public.

Ghodsi declined to share timing around a possible IPO, and it isn’t clear whether the company will pursue a traditional IPO or if it will continue to raise private funds so that it can direct list when it chooses to float. Regardless, Databricks is now sufficiently valuable that it can only exit to one of a handful of mega-cap technology giants or go public.

Why hasn’t the company gone public? Ghodsi is enjoying a rare position in the startup market: He has access to unlimited capital. Databricks had to open another $100 million in its latest round, which was originally set to close at just $1.5 billion. It doesn’t lack for investor interest, allowing its CEO to bring aboard the sort of shareholder he wants for his company’s post-IPO life — while enjoying limited dilution.

This also enables him to hire aggressively, possibly buy some smaller companies to fill in holes in Databricks’ product roadmap, and grow outside of the glare of Wall Street expectations from a position of capital advantage. It’s the startup equivalent of having one’s cake and eating it too.

But staying private longer isn’t without risks. If the larger market for software companies was rapidly devalued, Databricks could find itself too expensive to go public at its final private valuation. However, given the long bull market that we’ve seen in recent years for software shares, and the confidence Ghodsi has in his potential market, that doesn’t seem likely.

There’s still much about Databricks’ financial position that we don’t yet know — its gross margin profile, for example. TechCrunch is also incredibly curious what all its fundraising and ensuing spending have done to near-term Databricks operating cash flow results, as well as how long its gross-margin adjusted CAC payback has evolved since the onset of COVID-19. If we ever get an S-1, we might find out.

For now, winsome private markets are giving Ghodsi and crew space to operate an effectively public company without the annoyances that come with actually being public. Want the same thing for your company? Easy: Just reach $600 million ARR while growing 75% year over year.

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

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Databricks, the open-source data lake and data management powerhouse has been on quite a financial run lately. Today Bloomberg reported the company could be raising a new round worth at least $1.5 billion at an otherworldly $38 billion valuation. That price tag is up $10 billion from its last fundraise in February when it snagged $1 billion at a $28 billion valuation.

Databricks declined to comment on the Bloomberg post and its possible new valuation.

The company has been growing like gangbusters, giving credence to the investor thesis that the more your startup makes, the more it is likely to make. Consider that Databricks closed 2020 with $425 million in annual recurring revenue, which in itself was up 75% from the previous year.

As revenue goes up so does valuation, and Databricks is a great example of that rule in action. In October 2019, the company raised $400 million at a seemingly modest $6.2 billion valuation (if a valuation like that can be called modest). By February 2021, that had ballooned to $28 billion, and today it could be up to $38 billion if that rumor turns out to be true.

One of the reasons that Databricks is doing so well is it operates on a consumption model. The more data you move through the Databricks product family, the more money it makes, and with data exploding, it’s doing quite well, thank you very much.

It’s worth noting that Databricks’s primary competitor, Snowflake went public last year and has a market cap of almost $83 billion. In that context, the new figure doesn’t feel quite so outrageous, But what does it mean in terms of revenue to warrant a valuation like that. Let’s find out.

Valuation math

Let’s rewind the clock and observe the company’s recent valuation marks and various revenue results at different points in time:

• Q3 2019: $200 million run rate, $6.2 billion valuation
• Q3 2020: $350 million run rate, no known valuation change
• EoY 2020: $425 million run rate, $28 billion valuation (Q1 valuation)
• Q3 2021: Unclear run rate, possible $38 billion valuation

The company’s 2019 venture round gave Databricks a 31x run rate multiple. By the first quarter of 2021, that had swelled to a roughly 66x multiple if we compare its final 2020 revenue pace to its then-fresh valuation. Certainly software multiples were higher at the start of 2021 than they were in late 2019, but Databricks’s $28 billion valuation was still more than impressive; investors were betting on the company like it was going to be a key breakout winner, and a technology company that would go public eventually in a big way.

To see the company possibly raise more funds would therefore not be surprising. Presumably the company has had a good few quarters since its last round, given its history of revenue accretion. And there’s only more money available today for growing software companies than before.

But what to make of the $38 billion figure? If Databricks merely held onto its early 2021 run rate multiple, the company would need to have reached a roughly $575 million run rate, give or take. That would work out to around 36% growth in the last two-and-a-bit quarters. That works out to less than $75 million in new run rate per quarter since the end of 2020.

Is that possible? Yeah. The company added $75 million in run rate between Q3 2020 and the end of the year. So you can back-of-the-envelope the company’s growth to make a $38 billion valuation somewhat reasonable at a flat multiple. (There’s some fuzz in all of our numbers, as we are discussing rough timelines from the company; we’ll be able to go back and do more precise math once we get the Databricks S-1 filing in due time.)

All this raises the question of whether Databricks should be able to command such a high multiple. There’s some precedent. Recently, public software company Monday.com has a run rate multiple north of 50x, for example. It earned that mark on the back of a strong first quarter as a public company.

Databricks securing a higher multiple while private is not crazy, though we wonder if the data-focused company is managing a similar growth rate. Monday.com grew 94% on a year-over-year basis in its most recent quarter.

All this is to say that you can make the math shake out for Databricks to raise at a $38 billion valuation, but built into that price is quite a lot of anticipated growth. Top quartile public software companies today trade for around 23x their forward revenues, and around 27x their present-day revenues, per Bessemer. To defend its possible new valuation when public, then, leaves quite a lot of work ahead of Databricks.

The company’s CEO, Ali Ghodsi, will join us at TC Sessions: SaaS on October 27th, and we should know by then if this rumor is, indeed true. Either way, you can be sure we are going to ask him about it.

I worked at Google for six years. Internally, you have no choice — you must use Kubernetes if you are deploying microservices and containers (it’s actually not called Kubernetes inside of Google; it’s called Borg). But what was once solely an internal project at Google has since been open-sourced and has become one of the most talked about technologies in software development and operations.

For good reason. One person with a laptop can now accomplish what used to take a large team of engineers. At times, Kubernetes can feel like a superpower, but with all of the benefits of scalability and agility comes immense complexity. The truth is, very few software developers truly understand how Kubernetes works under the hood.

I like to use the analogy of a watch. From the user’s perspective, it’s very straightforward until it breaks. To actually fix a broken watch requires expertise most people simply do not have — and I promise you, Kubernetes is much more complex than your watch.

How are most teams solving this problem? The truth is, many of them aren’t. They often adopt Kubernetes as part of their digital transformation only to find out it’s much more complex than they expected. Then they have to hire more engineers and experts to manage it, which in a way defeats its purpose.

All of this means there is a great opportunity for DevOps startups to come in and address the different pain points within the Kubernetes ecosystem. This technology isn’t going anywhere, so any platform or tooling that helps make it more secure, simple to use and easy to troubleshoot will be well appreciated by the software development community.

In that sense, there’s never been a better time for VCs to invest in this ecosystem. It’s my belief that Kubernetes is becoming the new Linux: 96.4% of the top million web servers’ operating systems are Linux. Similarly, Kubernetes is trending to become the de facto operating system for modern, cloud-native applications. It is already the most popular open-source project within the Cloud Native Computing Foundation (CNCF), with 91% of respondents using it — a steady increase from 78% in 2019 and 58% in 2018.

While the technology is proven and adoption is skyrocketing, there are still some fundamental challenges that will undoubtedly be solved by third-party solutions. Let’s go deeper and look at five reasons why we’ll see a surge of startups in this space.

Containers are the go-to method for building modern apps

Docker revolutionized how developers build and ship applications. Container technology has made it easier to move applications and workloads between clouds. It also provides as much resource isolation as a traditional hypervisor, but with considerable opportunities to improve agility, efficiency and speed.

As the use of AI has grown and developed over the last several years, companies like Salesforce have tried to tap into it to improve their software and help customers operate faster and more efficiently. Kathy Baxter, principal architect for the ethical AI practice at Salesforce, will be joining us at TechCrunch Sessions: SaaS on October 27th to talk about the impact of AI on SaaS.

Baxter, who has more than 20 years of experience as a software architect, joined Salesforce in 2017 after more than a decade at Google in a similar role. We’re going to tap into her expertise on a panel discussing AI’s growing role in software.

Salesforce was one of the earlier SaaS adherents to AI, announcing its artificial intelligence tooling, which the company dubbed Einstein, in 2016. While the positioning makes it sound like a product, it’s actually much more than a single entity. It’s a platform component, which the various pieces of the Salesforce platform can tap into to take advantage of various types of AI to help improve the user experience.

That could involve feeding information to customer service reps on Service Cloud to make the call move along more efficiently, helping salespeople find the customers most likely to close a deal soon in the Sales Cloud or helping marketing understand the optimal time to send an email in the Marketing Cloud.

The company began building out its AI tooling early on with the help of 175 data scientists and has been expanding on that initial idea since. Other companies, both startups and established companies like SAP, Oracle and Microsoft, have continued to build AI into their platforms as Salesforce has. Today, many SaaS companies have some underlying AI built into their service.

Baxter will join us to discuss the role of AI in software today and how that helps improve the operations of the service itself, and what the implications are of using AI in your software service as it becomes a mainstream part of the SaaS development process.

In addition to our discussion with Baxter, the conference will also include Databricks’ Ali Ghodsi, UiPath’s Daniel Dines and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100, and use CrunchMatch to make expanding your empire quick, easy and efficient. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

Databricks launched its fifth open-source project today, a new tool called Delta Sharing designed to be a vendor-neutral way to share data with any cloud infrastructure or SaaS product, so long as you have the appropriate connector. It’s part of the broader Databricks open-source Delta Lake project.

As CEO Ali Ghodsi points out, data is exploding, and moving data from Point A to Point B is an increasingly difficult problem to solve with proprietary tooling. “The number one barrier for organizations to succeed with data is sharing data, sharing it between different views, sharing it across organizations — that’s the number one issue we’ve seen in organizations,” Ghodsi explained.

Delta Sharing is an open-source protocol designed to solve that problem. “This is the industry’s first-ever open protocol, an open standard for sharing a data set securely. […] They can standardize on Databricks or something else. For instance, they might have standardized on using AWS Data Exchange, Power BI or Tableau — and they can then access that data securely.”

The tool is designed to work with multiple cloud infrastructure and SaaS services and out of the gate there are multiple partners involved, including the Big Three cloud infrastructure vendors Amazon, Microsoft and Google, as well as data visualization and management vendors like Qlik, Starburst, Collibra and Alation and data providers like Nasdaq, S&P and Foursquare

Ghodsi said the key to making this work is the open nature of the project. By doing that and donating it to The Linux Foundation, he is trying to ensure that it can work across different environments. Another big aspect of this is the partnerships and the companies involved. When you can get big-name companies involved in a project like this, it’s more likely to succeed because it works across this broad set of popular services. In fact, there are a number of connectors available today, but Databricks expects that number to increase over time as contributors build more connectors to other services.

Databricks operates on a consumption pricing model much like Snowflake, meaning the more data you move through its software, the more money it’s going to make, but the Delta Sharing tool means you can share with anyone, not just another Databricks customer. Ghodsi says that the open-source nature of Delta Sharing means his company can still win, while giving customers more flexibility to move data between services.

The infrastructure vendors also love this model because the cloud data lake tools move massive amounts of data through their services and they make money too, which probably explains why they are all on board with this.

One of the big fears of modern cloud customers is being tied to a single vendor as they often were in the 1990s and early 2000s when most companies bought a stack of services from a single vendor like Microsoft, IBM or Oracle. On one hand, you had the veritable single throat to choke, but you were beholden to the vendor because the cost of moving to another one was prohibitively high. Companies don’t want to be locked in like that again and open source tooling is one way to prevent that.

Databricks was founded in 2013 and has raised almost $2 billion. The latest round was in February for $1 billion at a $28 billion valuation, an astonishing number for a private company. Snowflake, a primary competitor, went public last September. As of today, it has a market cap of over $66 billion.

Another hour, another billion-dollar round. That’s how February is kicking off. This time it’s Databricks, which just raised $1 billion Series G at a whopping $28 billion post-money valuation.

Databricks is a data-and-AI focused company that interacts with corporate information stored in the public cloud.

News of the new round began leaking last week. Franklin Templeton led the round, which also included new investors Fidelity and Whale Rock. Databricks also raised part of the capital from major cloud vendors including AWS, Alphabet via its CapitalG vehicle, and Salesforce Ventures. Microsoft is a previous investor, and it took part in the round as well.

But we’re not done! Other prior investors including a16z, T. Rowe Price, Tiger Global, BlackRock, and Coatue were also involved along with Alkeon Capital Management.

Consider that Databricks just raised a bushel of capital from a mix of cloud companies it works with, public investors it wants as shareholders when it goes public, and some private money that is enjoying a stiff markup from their last check into the company.

The company has made its mark with a series of four open source products with a core data lake product call Delta Lake leading the way. You may recall that another hot data lake company, Snowflake, raised almost a half a billion dollars on a $12.4 billion valuation a year ago before going public last September with a valuation twice that. Databricks has already exceeded that public valuation with this round — as a private company.

When we spoke to Databricks CEO Ali Ghodsi at the time of his company’s $400 million round in 2019, one which valued the company at $6.2 billion at the time, he said his company was the fastest growing enterprise cloud software companies ever, and that’s saying something.

The company makes money by offering each of those open source products as a software service and it’s doing exceedingly well at it, so much so that investors were tripping over each other to be part of this deal. In fact, Ghodsi said in a conversation with TechCrunch today that his company had targeted a much more modest $200 million raise, but that figure grew as more parties wanted to invest funds into the company. Even with that, Databricks had to turn capital away, he added, after deciding to cap the round at $1 billion.

The extra $800 million that the company raised will be used for M&A opportunities with an eye on talent, spend on establishing a Lakehouse concept, international expansion, while also expanding its engineering team, the CEO said.

Ghodsi also made clear that he does not intend to let the percentage of revenue that the company spends on R&D to drop, as is common at modern software companies — as many SaaS companies grow, they expend more of their revenue on sales and marketing efforts over product spend, something that Databricks wants to avoid by continuing to invest in engineering talent.

Why? Because Ghodsi says that the pace of innovation in AI is so rapid that IP becomes outdated in just a few years. That means that companies that want to lead in this space will have to stay on the bleeding edge of their market or fall back swiftly.

The Databricks model appears to be working well, with the company closing 2020 at $425 million in annual recurring revenue, or ARR. That figure, up 75% from the year-ago period, is also up from a $350 million run rate at the end of its Q3 2020. (For more on Databricks’ business, product and growth, head here.)

Notably Ghodsi told TechCrunch that this deal only started to come together in December. It’s February 1st today, which means that it took on this bushel of new funding remarkably quickly.

Finally, at $425 million in ARR, is the CEO worried about having a valuation sitting at roughly a 65x multiple? Ghodsi said that he is not. He said that he told his company during an all-hands earlier today that the AI market is a long journey, one that he hopes to be on for decades, and the stock market will go up and down. His point, as far as I could read into it, was that so long as Databricks keeps growing as it has, its valuation will take care of itself (and that seems to be the case so far with this company).

What’s certainly true is that Databricks is now as rich as it has ever been, as large as it has ever been, and in a market that is maturing. Let’s see what it can do with all this money.

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.

Data and analytics service Databricks today announced that it has acquired Redash, a company that helps data scientists and analysts visualize their data and build dashboards around it.

Redash’s customers include the likes of Atlassian, Cloudflare, Mozilla and Soundcloud and the company offers both an open-source self-hosted version of its tools, as well as paid hosted options.

The two companies did not disclose the financial details of the acquisition. According to Crunchbase, Tel Aviv-based Redash never raised any outside funding.

Databricks co-founder CEO Ali Ghodsi told me that the two companies met because one of his customers was using the product. “Since then, we’ve been impressed with the entire team and their attention to quality,” he said. “The combination of Redash and Databricks is really the missing link in the equation — an amazing backend with Lakehouse and an amazing front end built-in visualization and dashboarding feature from Redash to make the magic happen.”

Image Credits: Databricks

For Databricks, this is also a clear signal that it wants its service to become the go-to platform for all data teams and offer them all of the capabilities they would need to extract value from their data in a single platform.

“Not only are our organizations aligned in our open source heritage, but we also share in the mission to democratize and simplify data and AI so that data teams and more broadly, business intelligence users, can innovate faster,” Ghodsi noted. “We are already seeing awesome results for our customers in the combined technologies and look forward to continuing to grow together.”

In addition to the Redash acquisition, Databricks also today announced the launch of its Delta Engine, a new high-performance query engine for use with the company’s Delta Lake transaction layer.

“Databricks’ new Delta Engine for Delta Lake enables fast query execution for data analytics and data science, without moving the data out of the data lake,” the company explains. “The high-performance query engine has been built from the ground up to take advantage of modern cloud hardware for accelerated query performance. With this improvement, Databricks customers are able to move to a unified data analytics platform that can support any data use case and result in meaningful operational efficiencies and cost savings.”

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