AI lifecycle management startup launches free community tier, a data science startup headquartered in Jerusalem and New York, today released a community version of its machine learning automation platform designed to help enterprises manage and scale AI. CEO Yochay Ettun says the release was motivated in part by the influx of social distancing and remote work stemming from the COVID-19 pandemic, which has infected over 400,000 people to date.

“The release of CORE is our contribution to the strong data science community responsible for advancing AI innovation,” said Ettun. “CORE’s release marks a new vision for data science field. As data scientists, we built core to fill the need that so many data scientists require, to focus less on infrastructure and more on what they do best — algorithms.”

CORE facilitates machine learning workflow management with end-to-end AI model tracking and monitoring. Its built-in cluster orchestration supports hybrid cloud and multi-cloud configurations, and its compute querying and autoscaling — which can be fine-tuned from a dashboard — ensure that every available resource is fully utilized.

CORE can be installed on-premises or in a cloud environment directly from’s website. Developers can connect data sources to it to build and automatically retrain machine learning models; run machine learning experiments at scale to ensure reproducibility; and deploy to production with any framework or programming language.

There’s no shortage of orchestration platforms in the over $1.5 billion global machine learning market. Amazon recently rolled out SageMaker Studio, an extension of its SageMaker platform that automatically collects all code and project folders for machine learning in one place. Google offers its own solution in Cloud AutoML, which supports tasks like classification, sentiment analysis, and entity extraction as well as a range of file formats, including native and scanned PDFs. Not to be outdone, Microsoft recently introduced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps, and IBM has a comparable product in Watson Studio AutoAI.

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But two-year-old, which is backed by Jerusalem Venture Partners and private investors Kevin Bermeister and Prashagt Malik, has managed to raise $8 million in venture capital to date and attract customers including Nvidia, Sisense, NetApp, Lightricks, and

“As data scientists and AI consultants ourselves, we understand the frustration data scientists, data engineers and organizations encounter when building machine learning,” continued Ettun. “That is why we started, to help make the process faster and more efficient, so data scientists and developers can focus on the real magic — algorithms.”

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