Jun
02
2021
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Iterative raises $20M for its MLOps platform

Iterative, an open-source startup that is building an enterprise AI platform to help companies operationalize their models, today announced that it has raised a $20 million Series A round led by 468 Capital and Mesosphere co-founder Florian Leibert. Previous investors True Ventures and Afore Capital also participated in this round, which brings the company’s total funding to $25 million.

The core idea behind Iterative is to provide data scientists and data engineers with a platform that closely resembles a modern GitOps-driven development stack.

After spending time in academia, Iterative co-founder and CEO Dmitry Petrov joined Microsoft as a data scientist on the Bing team in 2013. He noted that the industry has changed quite a bit since then. While early on, the questions were about how to build machine learning models, today the problem is how to build predictable processes around machine learning, especially in large organizations with sizable teams. “How can we make the team productive, not the person? This is a new challenge for the entire industry,” he said.

Big companies (like Microsoft) were able to build their own proprietary tooling and processes to build their AI operations, Petrov noted, but that’s not an option for smaller companies.

Currently, Iterative’s stack consists of a couple of different components that sit on top of tools like GitLab and GitHub. These include DVC for running experiments and data and model versioning, CML, the company’s CI/CD platform for machine learning, and the company’s newest product, Studio, its SaaS platform for enabling collaboration between teams. Instead of reinventing the wheel, Iterative essentially provides data scientists who already use GitHub or GitLab to collaborate on their source code with a tool like DVC Studio that extends this to help them collaborate on data and metrics, too.

Image Credits: Iterative

“DVC Studio enables machine learning developers to run hundreds of experiments with full transparency, giving other developers in the organization the ability to collaborate fully in the process,” said Petrov. “The funding today will help us bring more innovative products and services into our ecosystem.”

Petrov stressed that he wants to build an ecosystem of tools, not a monolithic platform. When the company closed this current funding round about three months ago, Iterative had about 30 employees, many of whom were previously active in the open-source community around its projects. Today, that number is already closer to 60.

“Data, ML and AI are becoming an essential part of the industry and IT infrastructure,” said Leibert, general partner at 468 Capital. “Companies with great open-source adoption and bottom-up market strategy, like Iterative, are going to define the standards for AI tools and processes around building ML models.”

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

Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

Apr
09
2018
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Juro grabs $2M to take the hassle out of contracts

UK startup Juro, which is applying a “design centric approach” and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital.

Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise’s co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com).

Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round — with Point Nine Capital only joining “late” in the day.

“We actually could have strung it out to Series A,” he says of the funding that’s being announced now. “But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that it made sense to do a round now rather than wait for the A. The whole legal industry is undergoing radical change and we want to be leading it.”

Juro’s SaaS product is an integrated contracts workflow that combines contract creation, e-signing and commenting capabilities with AI-powered contract analytics.

Its general focus is on customers that have to manage a high volume of contacts — such as marketplaces.

The 2016-founded startup is not breaking out any customer numbers yet but says its client list includes the likes of Estee Lauder, Deliveroo and Nested. And Mabey adds that “most” of its demand is coming from enterprise at this point, noting it has “several tech unicorns and Fortune 500 companies in trial”.

While design is clearly a major focus — with the startup deploying clean-looking templates and visual cues to offer a user-friendly ‘upgrade’ on traditional legal processes — the machine learning component is its scalable, value-added differentiator to serve the target b2b users by helping them identify recurring sticking points in contract negotiations and keep on top of contract renewals.

Mabey tells TechCrunch the new funding will be used to double down on development of the machine learning component of the product.

“We’re not the first to market in contract management by about 25 years,” he says with a smilie. “So we have always needed to prove out our vision of why the incumbents are failing. One part of this is clunky UX and we’ve succeeded so far in replacing legacy providers through better design (e.g. we replace DocuSign at 80% of our customers).

“But the thing we and our investors are really excited about is not just helping businesses with contract workflow but helping them understand their contract data, auto-tag contracts, see pattens in negotiations and red flag unusual contract terms.”

While this machine learning element is where he sees Juro cutting out a competitive edge in an existing and established market, Mabey concedes it takes “quite a lot of capital to do well”. Hence taking more funding now.

“We need a level of predictive accuracy in our models that risk averse lawyers can get comfortable with and that’s a big ask!” he says.

Specifically, Juro will be using the funding to hire data scientists and machine learning engineers — building out the team at both its London and Riga offices. “We’re doing it like crazy,” adds Mabey. “For example, we just hired from the UK government Digital Service the data scientist who delivered the first ML model used by the UK government (on the gov.uk website).

“There is a huge opportunity here but great execution is key and we’re building a world class team to do it. It’s a big bet to grow revenue as quickly as we are and do this kind of R&D but that’s just what the market is demanding.”

Juro’s HQ remains in London for now, though Mabey notes its entire engineering team is based in the EU — between Riga, Amsterdam and Barcelona — “in part to avoid ‘Brexit risk’”.

“Only 27% of the team is British and we have customers operating in 12 countries — something I’m quite proud of — but it does leave us rather exposed. We’re very open minded about where we will be based in the future and are waiting to hear from the government on the final terms of Brexit,” he says when asked whether the startup has any plans to Brexit to Berlin.

“We always look beyond the UK for talent: if the government cannot provide certainty to our Romanian product designer (ex Kalo, Entrepreneur First) that she can stay in the UK post Brexit without risking a visa application, tbh it makes me less bullish on London!”

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