MLops
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Arrikto raises $10M for its MLOps platform
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
Image Credits: Arrikto
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”
Image Credits: Arrikto
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WhyLabs brings more transparancy to ML ops
WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.
The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.
Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.
“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”
Image Credits: WhyLabs
But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”
The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.
For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.
Image Credits: WhyLabs
According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.
“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”
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Enterprise companies find MLOps critical for reliability and performance
Rish Joshi
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Contributor
Rish is an entrepreneur and investor. Previously, he was a VC at Gradient Ventures (Google’s AI fund), co-founded a fintech startup building an analytics platform for SEC filings and worked on deep-learning research as a graduate student in computer science at MIT.
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Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.
What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.
That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.
Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.
Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.
As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.
We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.
The rise of MLOps
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