Arrikto raises $10M for its MLOps platform

Published November 16, 2020

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

Google Cloud wants to make it easier for data scientists to share models

Published November 8, 2018

Today, Google Cloud announced Kubeflow pipelines and AI Hub, two tools designed to help data scientists put to work across their organizations the models they create.

Rajen Sheth, director of product management for Google Cloud’s AI and ML products, says that the company recognized that data scientists too often build models that never get used. He says that if machine learning is really a team sport, as Google believes, models must get passed from data scientists to data engineers and developers who can build applications based on them.

To help fix that, Google is announcing Kubeflow pipelines, which are an extension of Kubeflow, an open-source framework built on top of Kubernetes designed specifically for machine learning. Pipelines are essentially containerized building blocks that people in the machine learning ecosystem can string together to build and manage machine learning workflows.

By placing the model in a container, data scientists can simply adjust the underlying model as needed and relaunch in a continuous delivery kind of approach. Sheth says this opens up even more possibilities for model usage in a company.

“[Kubeflow pipelines] also give users a way to experiment with different pipeline variants to identify which ones produce the best outcomes in a reliable and reproducible environment,” Sheth wrote in a blog post announcing the new machine learning features.

The company is also announcing AI Hub, which, as the name implies, is a central place where data scientists can go to find different kinds of ML content, including Kubeflow pipelines, Jupyter notebooks, TensorFlow modules and so forth. This will be a public repository seeded with resources developed by Google Cloud AI, Google Research and other teams across Google, allowing data scientists to take advantage of Google’s own research and development expertise.

But Google wanted the hub to be more than a public library — it also sees it as a place where teams can share information privately inside their organizations, giving it a dual purpose. This should provide another way to extend model usage by making essential building blocks available in a central repository.

AI Hub will be available in Alpha starting today with some initial components from Google, as well as tools for sharing some internal resources, but the plan is to keep expanding the offerings and capabilities over time.

Google believes if it provides easier ways to share model building blocks across an organization, the more likely they will be put to work. These tools are a step toward achieving that.