Arrikto’s mission is to enable data scientists to build and deploy their machine learning models faster. The company, which raised a $10 million Series A round in late 2020, is building its platform on top of Kubeflow, a cloud-native open-source project for building machine learning operations that was originally developed by Google but which is now mostly managed by the community. Until now, Arrikto’s main product was a self-managed enterprise distribution of Kubeflow for enterprises (aptly named ‘Enterprise Kubeflow’) that wanted to run it in their data centers or virtual private clouds. Today, the company is also launching a fully managed version of Kubeflow.
“Pushing ML models from experimentation all the way to production is incredibly complex,” Arrikto CEO and co-founder Constantinos Venetsanopoulos told me. “We see a few common reasons for this. Number one is data scientists are essentially not ops experts and ops people aren’t data scientists — and they don’t want to become data scientists. Second, we have seen an explosion of ML tools the last couple of years. They are extremely fragmented and they require a lot of integration. What we’re seeing is people struggling to stitch everything together. Both of those factors create a massive barrier to entry.”
With its fully managed Kubeflow, Arrikto aims to give businesses a platform that can help them accelerate their ML pipelines and free data scientists from having to worry about the infrastructure, while also allowing them to continue to use the tools they are already familiar with (think notebooks, TensorFlow, PyTorch, Hugging Face, etc.). “We want to break down the technical barrier that keeps most companies from deploying real machine learning capabilities,” said Venetsanopoulos.
With Kubeflow as a Service, the company argues, data scientists will get instant access to an end-to-end MLops platform. It’s essentially Arrikto’s Enterprise Kubeflow with a lot of custom automation tooling on top of it to abstract away all of the details of the Kubernetes platform it sits on top of.
For now, Arrikto will only run on a single cloud but in the long run, the plan is to support the three major cloud providers to ensure low latencies (and reduce the need to move lots of data between clouds).
Interestingly, Venetsanopoulos argues that the company’s biggest competitor right now isn’t other managed services like AWS’ SageMaker but businesses trying to build their own platforms by stitching together open-source tools.
“Kubeflow as a Service gives both data scientists and DevOps engineers the easiest way to use an MLOps platform on Kubernetes without having to request any infrastructure from their IT departments,” said Venetsanopoulos. “When an organization deploys Kubeflow in production – whether on-prem or in the cloud – Arrikto’s Kubeflow as a Service will turbocharge the process.”
The company, which now has about 60 employees, will continue to offer Kubeflow Enterprise in addition to this new fully managed service.
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