Machine Learning Operations, or MLOps, is a set of systems, tools, and practices that enables the deployment and operation of machine learning pipelines in production in a repeatable and trusted manner.
Together with Körber, Silo AI has developed a visual quality solution for inspection machines to control the quality of Covid19 vaccine ampoules.
In this article, I’ll shed a light on what are the ready-made configurable components in the area of Cloud AI & MLOps that we have built to speed up AI
Platforms thrive on the network effect, i.e, every new user creates value for other users of the platform. With data and AI, the network effect becomes even stronger. AI enables platform companies to learn faster than the competition, creating a virtuous circle.
Machine learning operations (MLOps) is about making machine learning development activities systematic and connecting the machine learning projects to the company’s business and IT infrastructure while bringing automation to the
Machine learning for predictive maintenance can provide a detailed risk assessment of potential failure events, and highlight situations with elevated risk. Combine it with a network of IoT sensors in your mission-critical equipment and 5G connectivity, and you’ll be on your way towards more reliable operations.
Dmitry Kan is a Lead AI Scientist in our Helsinki office, who can be described as a technical leader, founder, software engineer, researcher and entrepreneur.
To better serve our international client base, we’re next year focusing on building out AI labs in new markets, with Teppo Kuisma as our lead for internationalization. Teppo joins us after having spent a few years in the Bay Area, and is now focusing full speed on building new sites to create valuable long-term partnerships to better serve both new and existing international clients.
Machine learning and computer vision offers tremendous opportunities for modern healthcare technology. Together with the healthcare technology company Philips, we developed computer vision-driven solutions.
What are the paths and pitfalls in taking AI into production? How do you turn the first success into the next three? Taking an AI solution into production requires a
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