Databricks
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Why one Databricks investor thinks the company may be undervalued
The recent Databricks funding round, a $1 billion investment at a $28 billion valuation, was one of the year’s most notable private investments so far.
For Databricks signaled its IPO readiness by disclosing to TechCrunch last year that it had scaled its revenue run rate from $200 million to $350 million in a year, so the new capital looked like the capstone on its private fundraising before an eventual public debut.
The Exchange explores startups, markets and money. Read it every morning on Extra Crunch, or get The Exchange newsletter every Saturday.
But I did have a few questions, starting with the price of the round.
At a $28 billion valuation and ARR of $425 million, Databricks is valued at around 66x top line. That’s steep, if not the highest number we can dredge up on the public markets. Of course, for Databricks shareholders, seeing the value of their stock rise so quickly is hardly a bad thing. They are hardly going to complain about having more paper wealth.
But what about the investor perspective? Does the price really make sense? The Exchange caught up with Battery Ventures’ Dharmesh Thakker earlier this week to discuss a number of things, one of which was Databricks’ round and pricing. Thakker is named in the Databricks Series D funding announcement, which brought Battery into the company.
What was surprising about our conversation was not that Thakker was bullish on Databricks — a company that he and his firm have backed since its $140 million, 2017 round when the company was worth just under $1 billion. What surprised me was that he thinks its new $28 billion valuation might be a little low.
Intriguing, yeah? So this morning for both of us, I’ve pulled out quotes from our chat to help explain how Thakker views the market for Databricks, unicorns at scale more broadly through the lens of risk-adjusted investing, and the scale of the market some unicorns are playing in.
At the close, we’ll remind ourselves what Databricks CEO Ali Ghodsi told TechCrunch when we asked him the same question. Let’s go!
Databricks at $28 billion
Here’s how the valuation part of my chat with the Battery Ventures’ investor went down:
The Exchange: I want to talk about Databricks, because I spoke to [CEO] Ali [Ghodsi] yesterday about this round, and hot damn, it’s a lot of money at a valuation that is roughly 64x ARR, give or take. I don’t understand the price, and I know it’s a boring thing to talk about. [It’s a] great company, I get their market, I’ve talked to them a bunch, I know their revenue numbers. [But] I don’t understand the price, and I was hoping you could tell me why I’m being too conservative.
Dharmesh Thakker: I, for what it’s worth, think [the price] fair. If anything, I think it is on the lower end — he could have done better, frankly. But I think it comes down to three major things, right?
One is the addressable market. Just think about the addressable market of data. If there’s a trillion dollars spent in software or technology, I think you and I would be both hard pressed to say, almost all of that [isn’t] influenced by some data-oriented decisioning. Whether it’s digital transformation, whether it’s analytics, data is everywhere. So the TAM is massive … I think you and I both agree on that, whether it is $20 billion or $80 billion — it’s massive.
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Databricks raises $1B at $28B valuation as it reaches $425M ARR
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.
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Databricks launches SQL Analytics
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.
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As tech stocks rally, bring on the IPOs
During yesterday’s tense voting and this morning, shares of American-listed technology companies are shooting higher.
The tech-heavy Nasdaq composite is up around 3.35% this morning, more than double what the broad S&P 500 index is currently managing. SaaS and cloud stocks kicked off the day up a staggering 4.98%, a sharp rally in the value of smaller, more growth-oriented technology companies.
The Exchange explores startups, markets and money. Read it every morning on Extra Crunch, or get The Exchange newsletter every Saturday.
For technology companies on the wings of the IPO market, it’s great news.
In 2020 it can be easy to forget, but tech stocks do not have to rise. They merely have in recent months, perhaps warming the waters for more technology debuts as the fourth quarter races toward its midpoint. The Exchange has heard whispers from several folks that the late-November/early-December period could be active for new filings, bringing rising stocks and pent-up demand together for a possible IPO run.
We’ll see. Today’s rally — and ballot measure results in California — could be the push companies like Airbnb and DoorDash needed to stop faffing around with private filings.
In pedestrian terms, the getting is good right now for public tech companies, so if you are going to go public, go get got while the getting stays good.
Today, let’s examine recent market gains for tech stocks and remind ourselves who is expected to go public next. Then, of course, chat about all the unicorns on the unofficial IPO list who could find a greased path ahead of them toward a flotation.
Gains
Big tech stocks are gaining, small stocks are up and software companies are hot. The NASDAQ is now less than 5% away from its all-time highs, and the Bessemer Cloud Index is now just 9% down from its own, a rebound from its prior status in correction territory. (A correction occurs when an index falls 10% or more from highs.)
So, who does the rally help? Let’s rock through a list:
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What would Databricks be worth in a 2021 IPO?
TechCrunch recently covered Databricks’ financial performance in 2020, contrasting its recent performance to some historical 2019 data that the company shared.
The data-and-analysis-focused unicorn grew its annual run rate 75% to $350 million, compared to its year-ago quarter, meaning that the firm is growing well at scale. TechCrunch described it as “an obvious IPO candidate” at the time, a little under two weeks ago.
The Exchange explores startups, markets and money. Read it every morning on Extra Crunch, or get The Exchange newsletter every Saturday.
Since that point, Bloomberg reported that Databricks is indeed charging ahead with an IPO, a transaction that could come as soon as the first half of 2021, writing that it “has held talks with banks but has yet to hire underwriters” for its flotation.
That is enough news for us to have fun with. So, this morning let’s collate all that we know about the company’s financial performance, mix in some current market valuation metrics, and do some light projecting of Databricks’ growth. Our question? What might the company be worth at the end of Q1 or Q2 next year.
Of course, there are some worrying signs on the horizon that the stock market is about to shift lower, but, hey, there’s no need to be a pessimist this early on a Monday morning. Let’s get into the math.
Databricks’ potential IPO valuations
Starting with some history, Databricks was worth $6.2 billion after its September, 2019 Series F round of capital. The company raised $400 million in the transaction, its largest round to-date by $150 million. That capital should get the company to an H1 2020 IPO, provided that its spending didn’t go all old-school Dropbox.
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Equity Monday: SAP’s warning, and IPO updates for both Airbnb and Databricks
Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast where we unpack the numbers behind the headlines.
This is Equity Monday, our weekly kickoff that tracks the latest big news, chats about the coming week, digs into some recent funding rounds and mulls over a larger theme or narrative from the private markets. You can follow the show on Twitter here and myself here — and don’t forget to check out last Friday’s episode that includes some high-quality Quibi jokes, if I recall correctly.
This was a busy morning, with lots to talk about it. Here’s what we got into:
- Headlines this morning that caught our eye included falling global stock markets in the wake of rising COVID-19 cases; SAP torched tens of billions of market cap by missing earnings expectations and cutting its forecast; and Ant Group will raise a bajillion dollars in its impending IPO.
- In unicorn news, Databricks is prepping an IPO that we already know a lot about. And Airbnb’s shares are splitting ahead of its own IPO, a deal that we are expecting any day now.
- The PrimaryBid round was super cool, with TechCrunch covering the $50 million deal. I spoke with the company’s CEO last week about the deal, sharing some of those notes on the show. And VSPN raised nearly $100 million in an esports round that was more than cool.
- On the SAP front, here’s the question: Is the earnings miss and forecast miss a one-off, or something that a number of tech companies are going to suffer from? If the former, it’s not that big of a deal. If the latter, SAP could be the harbinger of a reset in tech valuations.
Shout-out to Lewis Hamilton and that G2 series. OK, chat Thursday!
Equity drops every Monday at 7:00 a.m. PT and Thursday afternoon as fast as we can get it out, so subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts.
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VCs reload ahead of the election as unicorns power ahead
This is The TechCrunch Exchange, a newsletter that goes out on Saturdays, based on the column of the same name. You can sign up for the email here.
It was an active week in the technology world broadly, with big news from Facebook and Twitter and Apple. But past the headline-grabbing noise, there was a steady drumbeat of bullish news for unicorns, or private companies worth $1 billion or more.
A bullish week for unicorns
The Exchange spent a good chunk of the week looking into different stories from unicorns, or companies that will soon fit the bill, and it’s surprising to see how much positive financial news there was on tap even past what we got to write about.
Databricks, for example, disclosed a grip of financial data to TechCrunch ahead of regular publication, including the fact that it grew its annual run rate (not ARR) to $350 million by the end of Q3 2020, up from $200 million in Q2 2019. It’s essentially IPO ready, but is not hurrying to the public markets.
Sticking to our theme, Calm wants more money for a huge new valuation, perhaps as high as $2.2 billion which is not a surprise. That’s more good unicorn news. As was the report that “India’s Razorpay [became a] unicorn after its new $100 million funding round” that came out this week.
Razorpay is only one of a number of Indian startups that have become unicorns during COVID-19. (And here’s another digest out this week concerning a half-dozen startups that became unicorns “amidst the pandemic.”)
There was enough good unicorn news lately that we’ve lost track of it all. Things like Seismic raising $92 million, pushing its valuation up to $1.6 billion from a few weeks ago. How did that get lost in the mix?
All this matters because while the IPO market has captured much attention in the last quarter or so, the unicorn world has not sat still. Indeed, it feels that unicorn VC activity is the highest we’ve seen since 2019.
And, as we’ll see in just a moment, the grist for the unicorn mill is getting refilled as we speak. So, expect more of the same until something material breaks our current investing and exit pattern.
Market Notes
What do unicorns eat? Cash. And many, many VCs raised cash in the last seven days.
A partial list follows. It could be that investors are looking to lock in new funds before the election and whatever chaos may ensue. So, in no particular order, here’s who is newly flush:
- $450 million for OpenView, $800 million for Canaan, $840 million for True Ventures, $950 million for Lead Edge Capital
- Something called Benson Capital Partners has put together a $50 million fund. Gayle Benson, for whom the firm is named, owns several New Orleans sports teams, per Forbes.
- Plus Venture Capital, built by two former 500 Startups Mena investors according to fundsglobalMENA, has raised $60 million.
- First Round is looking for $220 million, former Google exec Kai-Fu Lee’s Sinovation Ventures is looking for a billion, while Khosla wants a bit more.
All that capital needs to go to work, which means lots more rounds for many, many startups. The Exchange also caught up with a somewhat new firm this week: Race Capital. Helmed by Alfred Chuang, formerly or BEA who is an angel investor now in charge of his own fund, the firm has $50 million to invest.
Sticking to private investments into startups for the moment, quite a lot happened this week that we need to know more about. Like API-powered Argyle raising $20 million from Bain Capital Ventures for what FinLedger calls “unlocking and democratizing access to employment records.” TechCrunch is currently tracking the progress of API-led startups.
On the fintech side of things, M1 Finance raised $45 million for its consumer fintech platform in a Series C, while another roboadvisor, Wealthsimple, raised $87 million, becoming a unicorn at the same time. And while we’re in the fintech bucket, Stripe dropped $200 million this week for Nigerian startup Paystack. We need to pay more attention to the African startup scene. On the smaller end of fintech, Alpaca raised $10 million more to help other companies become Robinhood.
A few other notes before we change tack. Kahoot raised $215 million due to a boom in remote education, another trend that is inescapable in 2020 as part of the larger edtech boom (our own Natasha Mascarenhas has more).
Turning from the private market to the public, we have to touch on SPACs for just a moment. The Exchange got on the phone this week with Toby Russell from Shift, which is now a public company, trading after it merged with a SPAC, namely Insurance Acquisition Corp. Early trading is only going so well, but the CEO outlined for us precisely why he pursued a SPAC, which was actually interesting:
- Shift could have gone public via an IPO, Russell said, but prioritized a SPAC-led debut because his firm wanted to optimize for a capital raise to keep the company growing.
- How so? The private investment in public equity (PIPE) that the SPAC option came with ensured that Shift would have hundreds of millions in cash.
- Shift also wanted to minimize what the CEO described as market risk. A SPAC deal could happen regardless of what the broader markets were up to. And as the company made the choice to debut via a SPAC in April, some caution, we reckon, may have made some sense.
So now Shift is public and newly capitalized. Let’s see what happens to its shares as it gets into the groove of reporting quarterly. (Obviously, if it flounders, it’s a bad mark for SPACs, but, conversely, successful trading could lead to a bit more momentum to SPAC-mageddon.)
A few more things and we’re done. Unicorn exits had a good week. First, Datto’s IPO continues to move forward. It set an initial price this week, which could value it above $4 billion. Also this week, Roblox announced that it has filed to go public, albeit privately. It’s worth billions as well. And finally, DoubleVerify is looking to go public for as much as $5 billion early next year.
Not all liquidity comes via the public markets, as we saw this week’s Twilio purchase of Segment, a deal that The Exchange dug into to find out if it was well-priced or not.
Various and Sundry
We’re running long naturally, so here are just a few quick things to add to your weekend mental tea-and-coffee reading!
- This Operator Collective + @BLCKVC + @SalesforceVC mashup caught our attention.
- Accel has notes here on the EU startup scene, especially its later stages.
- Here’s a TechCrunch piece we helped put together that digs into the current state of media startups. It’s fun!
- Bytedance charts for your education and entertainment.
- Here’s where you can track the growth of DuckDuckGo as it takes on Google and Bing.
- Equity was a bundle of fun this week, so make sure to tune in if you have 30 minutes of free time.
Next week we are digging more deeply into Q3 venture capital data, a foretaste of which you can find here, regarding female founders, a topic that we returned to Friday in more depth.
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Databricks crossed $350M run rate in Q3, up from $200M one year ago
The Exchange regularly covers companies as they approach and crest the $100 million revenue mark. Our goal in tracking startups growing at scale is to scout future IPO candidates and better understand the late-stage financing market.
Today we’re digging into a company that is a little bit bigger than that. Namely Databricks, a data analytics company that was most recently valued at around $6.2 billion in its October, 2019 Series F when it raised $400 million.
The Exchange explores startups, markets and money. Read it every morning on Extra Crunch, or get The Exchange newsletter every Saturday.
The former startup reached a run rate of around $350 million at the end of Q3 2020, up from $200 million in revenue in Q3 2019, putting it on a rapid growth pace for a former startup of its size.
To better dig into the company’s performance, I got on the phone with its CEO, Ali Ghodsi, hoping to better understand how Databricks has managed to grow as much as it has in recent years. Ghodsi took over as CEO in 2016 after serving as the company’s VP of engineering. He’s also a co-founder.
Databricks is an obvious IPO candidate, but it’s also a company with broad private-market options, given its revenue expansion and attractive economics. Today, let’s talk about Databricks’ growth history, how it changed its sales process and what’s ahead for the unicorn more than six times over.
What does Databricks do?
What does Databricks actually do? Normally I’d be content to wave my hands at data analytics and call it a day. Chatting with Ghodsi, however, clarified the matter, so let me help.
Let’s say that a company has a lot of data on its machinery and wants to know when different pieces are going to fail. Or, perhaps a company wants to find patterns in some economic data. How do they find that information?
Ghodsi reckons you need three things: First, data engineering, or getting customer data “massaged into the right forms so that you can actually start using it.” Second, data science, which Ghodsi describes as “the machine learning algorithms, the predictive algorithms that you need to have.” And third, on top, companies “more and more” also want data warehousing and some “basic analytics,” he added.
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Databricks acquires Redash, a visualizations service for data scientists
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.”
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Enterprise companies find MLOps critical for reliability and performance
Rish Joshi
Contributor
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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