How to Scale End-to-End Observability in AWS Environments

Arrikto raises 10 million to advance its ML platform

Dec. 15, 2020, 9:46 a.m.

TL;DR

Led by Unusual Ventures, Arrikto has raised $10 million in Series A funding to advance its ML platform. The startup has also added John Vrionis, Co-Founder and Managing Partner of Unusual Ventures, to its board.

Arrikto’s technology makes deploying and managing Kubeflow simpler

Key Facts

1
Founded in 2015, Arrikto aims to speed up the Machine Learning development lifecycle by allowing data scientists to treat data as code.
2
Arrikto makes it easier to set up end-to-end ML learning pipelines, and build, train, and deploy ML models into production using Kubernetes. It aims to break down the technical barriers that prevent many companies from implementing large-scale ML capabilities.
3
Arrikto uses Kubeflow, the open-source ML toolkit for Kubernetes, at its core. The team has also built MiniKF that runs Kubeflow using Kale, allowing engineers to build Kubeflow pipelines from their Jupyterlab notebooks.
4
The startup has already amassed more than 100 customers in a short span, including one of the world’s largest oil and gas companies.

Details

Arrikto’s technology makes deploying and managing Kubeflow simpler, allowing data scientists to manage it using the tools they are already familiar with. It also helps create a portable environment for data science to enable data sharing and data versioning across teams and clouds. You can quickly build models on your laptop, GCP, or AWS with MiniKF, an all-in-one, single-node Kubeflow distribution containing Minikube, Kale, and Rok.

With Arrikto, data scientists can easily generate production-ready pipelines for MLOps. It allows you to create pipeline components and KFP DSL, resolve dependencies, inject data objects into each step, and deploy the pipeline swiftly. You can also tag cells in Jupyter to define pipeline steps, hyperparameter tuning, GPU usage, and metrics tracking.

It is also possible to roll back to any pipeline step at its exact execution state to debug. You may also collaborate with other data scientists through a GitOps-style publish versioning workflow.

Arrikto also allows you to isolate ML data access within the user’s namespace while allowing notebook and pipeline collaboration in shared namespaces. Isolating users and their data with RBAC and fine-grain authorization access ensure secure access.

Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications. 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.
Constantinos Venetsanopoulos
CEO and Co-founder of Arrikto