Mar
29
2021
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Testing platform Tricentis acquires performance testing service Neotys

If you develop software for a large enterprise company, chances are you’ve heard of Tricentis. If you don’t develop software for a large enterprise company, chances are you haven’t. The software testing company with a focus on modern cloud and enterprise applications was founded in Austria in 2007 and grew from a small consulting firm to a major player in this field, with customers like Allianz, BMW, Starbucks, Deutsche Bank, Toyota and UBS. In 2017, the company raised a $165 million Series B round led by Insight Venture Partners.

Today, Tricentis announced that it has acquired Neotys, a popular performance testing service with a focus on modern enterprise applications and a tests-as-code philosophy. The two companies did not disclose the price of the acquisition. France-based Neotys launched in 2005 and raised about €3 million before the acquisition. Today, it has about 600 customers for its NeoLoad platform. These include BNP Paribas, Dell, Lufthansa, McKesson and TechCrunch’s own corporate parent, Verizon.

As Tricentis CEO Sandeep Johri noted, testing tools were traditionally script-based, which also meant they were very fragile whenever an application changed. Early on, Tricentis introduced a low-code tool that made the automation process both easier and resilient. Now, as even traditional enterprises move to DevOps and release code at a faster speed than ever before, testing is becoming both more important and harder for these companies to implement.

“You have to have automation and you cannot have it be fragile, where it breaks, because then you spend as much time fixing the automation as you do testing the software,” Johri said. “Our core differentiator was the fact that we were a low-code, model-based automation engine. That’s what allowed us to go from $6 million in recurring revenue eight years ago to $200 million this year.”

Tricentis, he added, wants to be the testing platform of choice for large enterprises. “We want to make sure we do everything that a customer would need, from a testing perspective, end to end. Automation, test management, test data, test case design,” he said.

The acquisition of Neotys allows the company to expand this portfolio by adding load and performance testing as well. It’s one thing to do the standard kind of functional testing that Tricentis already did before launching an update, but once an application goes into production, load and performance testing becomes critical as well.

“Before you put it into production — or before you deploy it — you need to make sure that your application not only works as you expect it, you need to make sure that it can handle the workload and that it has acceptable performance,” Johri noted. “That’s where load and performance testing comes in and that’s why we acquired Neotys. We have some capability there, but that was primarily focused on the developers. But we needed something that would allow us to do end-to-end performance testing and load testing.”

The two companies already had an existing partnership and had integrated their tools before the acquisition — and many of its customers were already using both tools, too.

“We are looking forward to joining Tricentis, the industry leader in continuous testing,” said Thibaud Bussière, president and co-founder at Neotys. “Today’s Agile and DevOps teams are looking for ways to be more strategic and eliminate manual tasks and implement automated solutions to work more efficiently and effectively. As part of Tricentis, we’ll be able to eliminate laborious testing tasks to allow teams to focus on high-value analysis and performance engineering.”

NeoLoad will continue to exist as a stand-alone product, but users will likely see deeper integrations with Tricentis’ existing tools over time, include Tricentis Analytics, for example.

Johri tells me that he considers Tricentis one of the “best kept secrets in Silicon Valley” because the company not only started out in Europe (even though its headquarters is now in Silicon Valley) but also because it hasn’t raised a lot of venture rounds over the years. But that’s very much in line with Johri’s philosophy of building a company.

“A lot of Silicon Valley tends to pay attention only when you raise money,” he told me. “I actually think every time you raise money, you’re diluting yourself and everybody else. So if you can succeed without raising too much money, that’s the best thing. We feel pretty good that we have been very capital efficient and now we’re recognized as a leader in the category — which is a huge category with $30 billion spend in the category. So we’re feeling pretty good about it.”

Jan
15
2021
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GitLab oversaw a $195 million secondary sale that values the company at $6 billion

GitLab has confirmed with TechCrunch that it oversaw a $195 million secondary sale that values the company at $6 billion. CNBC broke the story earlier today.

The company’s impressive valuation comes after its most recent 2019 Series E in which it raised $268 million on a 2.75 billion valuation, an increase of $3.25 billion in under 18 months. Company co-founder and CEO Sid Sijbrandij believes the increase is due to his company’s progress adding functionality to the platform.

“We believe the increase in valuation over the past year reflects the progress of our complete DevOps platform towards realizing a greater share of the growing, multi-billion dollar software development market,” he told TechCrunch.

While the startup has raised over $434 million, this round involved buying employee stock options, a move that allows the company’s workers to cash in some of their equity prior to going public. CNBC reported that the firms buying the stock included Alta Park, HMI Capital, OMERS Growth Equity, TCV and Verition.

The next logical step would appear to be IPO, something the company has never shied away from. In fact, it actually at one point included the proposed date of November 18, 2020 as a target IPO date on the company wiki. While they didn’t quite make that goal, Sijbrandij still sees the company going public at some point. He’s just not being so specific as in the past, suggesting that the company has plenty of runway left from the last funding round and can go public when the timing is right.

“We continue to believe that being a public company is an integral part of realizing our mission. As a public company, GitLab would benefit from enhanced brand awareness, access to capital, shareholder liquidity, autonomy and transparency,” he said.

He added, “That said, we want to maximize the outcome by selecting an opportune time. Our most recent capital raise was in 2019 and contributed to an already healthy balance sheet. A strong balance sheet and business model enables us to select a period that works best for realizing our long-term goals.”

GitLab has not only published IPO goals on its Wiki, but its entire company philosophy, goals and OKRs for everyone to see. Sijbrandij told TechCrunch’s Alex Wilhelm at a TechCrunch Disrupt panel in September that he believes that transparency helps attract and keep employees. It doesn’t hurt that the company was and remains a fully remote organization, even pre-COVID.

“We started [this level of] transparency to connect with the wider community around GitLab, but it turned out to be super beneficial for attracting great talent as well,” Sijbrandij told Wilhelm in September.

The company, which launched in 2014, offers a DevOps platform to help move applications through the programming lifecycle.

Update: The original headline of this story has been changed from ‘GitLab raises $195M in secondary funding on $6 billion valuation.’

 

Dec
07
2020
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3 questions to ask before adopting microservice architecture

As a product manager, I’m a true believer that you can solve any problem with the right product and process, even one as gnarly as the multiheaded hydra that is microservice overhead.

Working for Vertex Ventures US this summer was my chance to put this to the test. After interviewing 30+ industry experts from a diverse set of companies — Facebook, Fannie Mae, Confluent, Salesforce and more — and hosting a webinar with the co-founders of PagerDuty, LaunchDarkly and OpsLevel, we were able to answer three main questions:

  1. How do teams adopt microservices?
  2. What are the main challenges organizations face?
  3. Which strategies, processes and tools do companies use to overcome these challenges?

How do teams adopt microservices?

Out of dozens of companies we spoke with, only two had not yet started their journey to microservices, but both were actively considering it. Industry trends mirror this as well. In an O’Reilly survey of 1500+ respondents, more than 75% had started to adopt microservices.

It’s rare for companies to start building with microservices from the ground up. Of the companies we spoke with, only one had done so. Some startups, such as LaunchDarkly, plan to build their infrastructure using microservices, but turned to a monolith once they realized the high cost of overhead.

“We were spending more time effectively building and operating a system for distributed systems versus actually building our own services so we pulled back hard,” said John Kodumal, CTO and co-founder of LaunchDarkly.

“As an example, the things we were trying to do in mesosphere, they were impossible,” he said. “We couldn’t do any logging. Zero downtime deploys were impossible. There were so many bugs in the infrastructure and we were spending so much time debugging the basic things that we weren’t building our own service.”

As a result, it’s more common for companies to start with a monolith and move to microservices to scale their infrastructure with their organization. Once a company reaches ~30 developers, most begin decentralizing control by moving to a microservice architecture.

Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there.

Large companies with established monoliths are keen to move to microservices, but costs are high and the transition can take years. Atlassian’s platform infrastructure is in microservices, but legacy monoliths in Jira and Confluence persist despite ongoing decomposition efforts. Large companies often get stuck in this transition. However, a combination of strong, top-down strategy combined with bottoms-up dev team support can help companies, such as Freddie Mac, make substantial progress.

Some startups, like Instacart, first shifted to a modular monolith that allows the code to reside in a single repository while beginning the process of distributing ownership of discrete code functions to relevant teams. This enables them to mitigate the overhead associated with a microservice architecture by balancing the visibility of having a centralized repository and release pipeline with the flexibility of discrete ownership over portions of the codebase.

What challenges do teams face?

Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there. John Laban, CEO and co-founder of OpsLevel, which helps teams build and manage microservices told us that “with a distributed or microservices based architecture your teams benefit from being able to move independently from each other, but there are some gotchas to look out for.”

Indeed, the linked O’Reilly chart shows how the top 10 challenges organizations face when adopting microservices are shared by 25%+ of respondents. While we discussed some of the adoption blockers above, feedback from our interviews highlighted issues around managing complexity.

The lack of a coherent definition for a service can cause teams to generate unnecessary overhead by creating too many similar services or spreading related services across different groups. One company we spoke with went down the path of decomposing their monolith and took it too far. Their service definitions were too narrow, and by the time decomposition was complete, they were left with 4,000+ microservices to manage. They then had to backtrack and consolidate down to a more manageable number.

Defining too many services creates unnecessary organizational and technical silos while increasing complexity and overhead. Logging and monitoring must be present on each service, but with ownership spread across different teams, a lack of standardized tooling can create observability headaches. It’s challenging for teams to get a single-pane-of-glass view with too many different interacting systems and services that span the entire architecture.

Dec
01
2020
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AWS announces DevOps Guru to find operational issues automatically

At AWS re:Invent today, Andy Jassy announced DevOps Guru, a new tool for DevOps teams to help the operations side find issues that could be having an impact on an application performance. Consider it like the sibling of CodeGuru, the service the company announced last year to find issues in your code before you deploy.

It works in a similar fashion using machine learning to find issues on the operations side of the equation. “I’m excited to launch a new service today called Amazon DevOps Guru, which is a new service that uses machine learning to identify operational issues long before they impact customers,” Jassy said today.

The way it works is that it collects and analyzes data from application metrics, logs, and events “to identify behavior that deviates from normal operational patterns,” the company explained in the blog post announcing the new service.

This service essentially gives AWS a product that would be competing with companies like Sumo Logic, DataDog or Splunk by providing deep operational insight on problems that could be having an impact on your application such as misconfigurations or resources that are over capacity.

When it finds a problem, the service can send an SMS, Slack message or other communication to the team and provides recommendations on how to fix the problem as quickly as possible.

What’s more, you pay for the data analyzed by the service, rather than a monthly fee. The company says this means that there is no upfront cost or commitment involved.

Oct
21
2020
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Contrast launches its security observability platform

Contrast, a developer-centric application security company with customers that include Liberty Mutual Insurance, NTT Data, AXA and Bandwidth, today announced the launch of its security observability platform. The idea here is to offer developers a single pane of glass to manage an application’s security across its lifecycle, combined with real-time analysis and reporting, as well as remediation tools.

“Every line of code that’s happening increases the risk to a business if it’s not secure,” said Contrast CEO and chairman Alan Naumann. “We’re focused on securing all that code that businesses are writing for both automation and digital transformation.”

Over the course of the last few years, the well-funded company, which raised a $65 million Series D round last year, launched numerous security tools that cover a wide range of use cases, from automated penetration testing to cloud application security and now DevOps — and this new platform is meant to tie them all together.

DevOps, the company argues, is really what necessitates a platform like this, given that developers now push more code into production than ever — and the onus of ensuring that this code is secure is now also often on that.

Image Credits: Contrast

Traditionally, Naumann argues, security services focused on the code itself and looking at traffic.

“We think at the application layer, the same principles of observability apply that have been used in the IT infrastructure space,” he said. “Specifically, we do instrumentation of the code and we weave security sensors into the code as it’s being developed and are looking for vulnerabilities and observing running code. […] Our view is: the world’s most complex systems are best when instrumented, whether it’s an airplane, a spacecraft, an IT infrastructure. We think the same is true for code. So our breakthrough is applying instrumentation to code and observing for security vulnerabilities.”

With this new platform, Contrast is aggregating information from its existing systems into a single dashboard. And while Contrast observes the code throughout its lifecycle, it also scans for vulnerabilities whenever a developers check code into the CI/CD pipeline, thanks to integrations with most of the standard tools like Jenkins. It’s worth noting that the service also scans for vulnerabilities in open-source libraries. Once deployed, Contrast’s new platform keeps an eye on the data that runs through the various APIs and systems the application connects to and scans for potential security issues there as well.

The platform currently supports all of the large cloud providers, like AWS, Azure and Google Cloud, and languages and frameworks, like Java, Python, .NET and Ruby.

Image Credits: Contrast

Jul
09
2020
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LogDNA announces $25M Series C investment and new CEO

LogDNA, a startup that helps DevOps teams dig through their log data to find issues, announced a $25 million Series C investment today along with the promotion of industry vet Tucker Callaway to CEO.

Let’s start with the funding. Emergence Capital led the round with participation from previous investors Initialized Capital and Providence Equity. New investors TI Platform Management, Radianx Capital, Top Tier Capital and Trend Forward Capital also joined the round. Today’s investment brings the total raised to $60 million, according to the company.

Current CEO and co-founder Chris Nguyen says the company provides a centralized way to manage log data for DevOps teams with an eye toward troubleshooting issues and getting applications out faster.

New CEO Callaway, whose background includes executive stints at Chef and Sauce Labs, came on board in January as president and CRO with an eye toward moving him into the top spot when the time was right. Nguyen, who will move to the role of chief strategy officer, says everyone was on board with the move, and he was ready to step back into a more technical role.

“When we closed the latest round of funding and looked at what the journey forward looks like, there was just a lot of trust and confidence from my co-founder, the board of directors, all of the investors on the team that Tucker is the right leader,” Nguyen said.

As Callaway takes over in the midst of the pandemic, the company is in reasonably good shape, with 3,000 customers using the product and a strategic partnership with IBM to provide logging services for IBM Cloud. Having $25 million in additional capital certainly helps, but he sees a company that’s still growing and intends to keep hiring.

As he brings more people on board to lead the company of approximately 100 employees, he says that diversity and inclusion is something he is passionate about and takes very seriously. For starters, he plans to put the entire company through unconscious bias training. They have also hired someone to review their hiring practices to date and they are bringing in a consultant to help them design more diverse and inclusive hiring practices and hold them accountable to that.

The company was a member of the same Y Combinator winter 2015 cohort as GitLab. It actually started out building a marketing technology product, only to realize they had built a powerful logging tool on the back end. That logging tool became the basis for LogDNA .

Jun
22
2020
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4 enterprise developer trends that will shape 2021

Technology has dramatically changed over the last decade, and so has how we build and deliver enterprise software.

Ten years ago, “modern computing” was to rely on teams of network admins managing data centers, running one application per server, deploying monolithic services, through waterfall, manual releases managed by QA and release managers.

Today, we have multi and hybrid clouds, serverless services, in continuous integration, running infrastructure-as-code.

SaaS has grown from a nascent 2% of the $450B enterprise software market in 2009, to 23% in 2020 and crossed $100B in revenue. PaaS and IaaS revenue represent another $50B in revenue, expecting to double to $100B by 2022.

With 77% of the enterprise software market — over $350B in annual revenue — still on legacy and on-premise systems, modern SaaS, PaaS and IaaS eating at the legacy market alone can grow the market 3x-4x over the next decade.

As the shift to cloud accelerates across the platform and infrastructure layers, here are four trends starting to emerge that will change how we develop and deliver enterprise software for the next decade.

1. The move to “everything as code”

Companies are building more dynamic, multiplatform, complex infrastructures than ever. We see the “-aaS” of the application, data, runtime and virtualization layers. Modern architectures are forcing extensibility to work with any number of mixed and matched services.

Jun
02
2020
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Atlassian launches new DevOps features

Atlassian today launched a slew of DevOps-centric updates to a variety of its services, ranging from Bitbucket Cloud and Pipelines to Jira and others. While it’s quite a grab-bag of announcements, the overall idea behind them is to make it easier for teams to collaborate across functions as companies adopt DevOps as their development practice of choice.

“I’ve seen a lot of these tech companies go through their agile and DevOps transformations over the years,” Tiffany To, the head of agile and DevOps solutions at Atlassian told me. “Everyone wants the benefits of DevOps, but — we know it — it gets complicated when we mix these teams together, we add all these tools. As we’ve talked with a lot of our users, for them to succeed in DevOps, they actually need a lot more than just the toolset. They have to enable the teams. And so that’s what a lot of these features are focused on.”

As To stressed, the company also worked with several ecosystem partners, for example, to extend the automation features in Jira Software Cloud, which can now also be triggered by commits and pull requests in GitHub, GitLab and other code repositories that are integrated into Jira Software Cloud. “Now you get these really nice integrations for DevOps where we are enabling these developers to not spend time updating the issues,” To noted.

Indeed, a lot of the announcements focus on integrations with third-party tools. This, To said, is meant to allow Atlassian to meet developers where they are. If your code editor of choice is VS Code, for example, you can now try Atlassian’s now VS Code extension, which brings your task like from Jira Software Cloud to the editor, as well as a code review experience and CI/CD tracking from Bitbucket Pipelines.

Also new is the “Your Work” dashboard in Bitbucket Cloud, which can now show you all of your assigned Jira issues, as well as Code Insights in Bitbucket Cloud. Code Insights features integrations with Mabl for test automation, Sentry for monitoring and Snyk for finding security vulnerabilities. These integrations were built on top of an open API, so teams can build their own integrations, too.

“There’s a really important trend to shift left. How do we remove the bugs and the security issues earlier in that dev cycle, because it costs more to fix it later,” said To. “You need to move that whole detection process much earlier in the software lifecycle.”

Jira Service Desk Cloud is getting a new Risk Management Engine that can score the risk of changes and auto-approve low-risk ones, as well as a new change management view to streamline the approval process.

Finally, there is new Opsgenie and Bitbucket Cloud integration that centralizes alerts and promises to filter out the noise, as well as a nice incident investigation dashboard to help teams take a look at the last deployment that happened before the incident occurred.

“The reason why you need all these little features is that as you stitch together a very large number of tools […], there is just lots of these friction points,” said To. “And so there is this balance of, if you bought a single toolchain, all from one vendor, you would have fewer of these friction points, but then you don’t get to choose best of breed. Our mission is to enable you to pick the best tools because it’s not one-size-fits-all.”

May
14
2020
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Venafi acquires Jetstack, the startup behind the cert-manager Kubernetes certificate controller

It seems that we are in the middle of a mini acquisition spree for Kubernetes startups, specifically those that can help with Kubernetes security. In the latest development, Venafi, a vendor of certificate and key management for machine-to-machine connections, is acquiring Jetstack, a U.K. startup that helps enterprises migrate and work within Kubernetes and cloud-based ecosystems, which has also been behind the development of cert-manager, a popular, open-source native Kubernetes certificate management controller.

Financial terms of the deal, which is expected to close in June of this year, have not been disclosed, but Jetstack has been working with Venafi to integrate its services and had a strategic investment from Venafi’s Machine Identity Protection Development Fund.

Venafi is part of the so-called “Silicon Slopes” cluster of startups in Utah. It has raised about $190 million from investors that include TCV, Silver Lake and Intel Capital and was last valued at $600 million. That was in 2018, when it raised $100 million, so now it’s likely Venafi is worth more, especially considering its customers include the top five U.S. health insurers, the top five U.S. airlines, the top four credit card issuers, three out of the top four accounting and consulting firms, four of the top five U.S., U.K., Australian and South African banks and four of the top five U.S. retailers.

For the time being, the two organizations will continue to operate separately, and cert-manager — which has hundreds of contributors and millions of downloads — will continue on as before, with a public release of version 1 expected in the June-July time frame.

The deal underscores not just how Kubernetes-based containers have quickly gained momentum and critical mass in the enterprise IT landscape, in particular around digital transformation, but specifically the need to provide better security services around that at speed and at scale. The deal comes just one day after VMware announced that it was acquiring Octarine, another Kubernetes security startup, to fold into Carbon Black (an acquisition it made last year).

“Nowadays, business success depends on how quickly you can respond to the market,” said Matt Barker, CEO and co-founder of Jetstack. “This reality led us to re-think how software is built and Kubernetes has given us the ideal platform to work from. However, putting speed before security is risky. By joining Venafi, Jetstack will give our customers a chance to build fast while acting securely.”

To be clear, Venafi had been offering Kubernetes integrations prior to this — and Venafi and Jetstack have worked together for two years. But acquiring Jetstack will give it direct, in-house expertise to speed up development and deployment of better tools to meet the challenges of a rapidly expanding landscape of machines and applications, all of which require unique certificates to connect securely.

“In the race to virtualize everything, businesses need faster application innovation and better security; both are mandatory,” said Jeff Hudson, CEO of Venafi, in a statement. “Most people see these requirements as opposing forces, but we don’t. We see a massive opportunity for innovation. This acquisition brings together two leaders who are already working together to accelerate the development process while simultaneously securing applications against attack, and there’s a lot more to do. Our mutual customers are urgently asking for more help to solve this problem because they know that speed wins, as long as you don’t crash.”

The crux of the issue is the sheer volume of machines that are being used in computing environments, thanks to the growth of Kubernetes clusters, cloud instances, microservices and more, with each machine requiring a unique identity to connect, communicate and execute securely, Venafi notes, with disruptions or misfires in the system leaving holes for security breaches.

Jetstack’s approach to information security came by way of its expertise in Kubernetes, developing cert-mananger specifically so that its developer customers could easily create and maintain certificates for their networks.

“At Jetstack we help customers realize the benefits of Kubernetes and cloud native infrastructure, and we see transformative results to businesses firsthand,” said Matt Bates, CTO and co-founder of Jetstack, in a statement. “We developed cert-manager to make it easy for developers to scale Kubernetes with consistent, secure, and declared-as-code machine identity protection. The project has been a huge hit with the community and has been adopted far beyond our expectations. Our team is thrilled to join Venafi so we can accelerate our plans to bring machine identity protection to the cloud native stack, grow the community and contribute to a wider range of projects across the ecosystem.” Both Bates and Barker will report to Venafi’s Hudson and join the bigger company’s executive team.

May
06
2020
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Enterprise companies find MLOps critical for reliability and performance

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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