Cloudian and Machine Box Partner to Deliver Machine-Learning Solutions that Make Content Quickly and Easily Searchable

ML-driven image recognition generates metadata for intelligent media search in object storage systems

SAN MATEO, Calif., May 22, 2018 — Cloudian, the innovation leader in enterprise object storage systems, today announced a partnership with Machine Box, makers of state-of-the-art machine-learning technology, to develop solutions that generate deep insight into data files in ways that are fast, accurate and cost-effective.

The two companies, which together demonstrated facial-recognition technology at the NAB Show, held April 9-12 in Las Vegas, are developing solutions that apply machine learning (ML) and artificial intelligence (AI) to media content. Employing facial recognition and other pattern-detection technologies, the joint solutions add metadata to storage objects, making it easier to search and retrieve content.

Machine Box’s ML technology can be trained to recognize any image type: a face, brand, logo, landmark, scene, context, gender or even inappropriate content. When the technology identifies such an image in content it can then add metadata about it to the storage object. Machine Box models can run anywhere, in the cloud or on-premises behind a firewall, eliminating the need to send private data to a public cloud endpoint for processing.

Machine Box provides both pre-trained and highly-trainable ML models, so users can tune the tagging and recognition over time using their own content. This approach offers significantly more accurate results than those of algorithms that rely on generic content.

Cloudian’s HyperStore object storage systems include embedded metadata tags, letting users label data and enabling search via integrated tools such as Elastic Search.

The combined solution enables content creators to quickly locate and monetize video assets; it helps producers, for example, identify trends and then quickly locate media that pertain to those trends. Production companies can also cut costs by quickly finding assets, rather than re-buying or re-shooting them.

“As organizations generate more and more digital media and seek new channels to monetize it, deep insight into media becomes critical,” said Aaron Edell, CEO of Machine Box. “Machine Box and Cloudian can help producers use the power of machine learning and limitlessly scalable, searchable storage to find the content they need, right now.”

“Media search is a major challenge for media professionals, as we saw at NAB this year, where 79 percent of the attendees surveyed indicated a desire to employ AI/ML techniques to accelerate the search process and improve results,” said Jon Toor, CMO at Cloudian. “Combining AI with object storage allows all content, whether created in the past, present or future, to gain added value for increased profits and greater content flow.”

About Cloudian
Cloudian turns information into insight with an infinitely scalable platform that consolidates, manages and protects enterprise data. Cloudian data management solutions bring cloud technology and economics to the data center with uncompromising data durability, intuitive management tools, and the industry’s most compatible S3 API. Cloudian and its ecosystem partners help Global 1000 customers simplify unstructured data management today, while preparing for the data demands of AI and machine learning tomorrow.

About Machine Box
Machine Box simplifies machine learning so that anyone can integrate, deploy and scale AI with very little effort. Inside its Docker containers are state-of-the-art machine learning models, with the built-in ability to teach, train and tune with minimal amounts of data. For instance, you can train face and image recognition starting with a single example. Current boxes include face and image recognition, natural language processing, nudity detection, content personalization and recommendation, custom image and text classification, and more. –

Join us on LinkedIn, follow us on Twitter (@CloudianStorage) and Facebook, or visit us at

Media Contact:
Rick Popko
[email protected]

Click to rate this post!
[Total: 0 Average: 0]