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.
Silo AI and UNAIDS, the Joint United Nations Program on HIV/AIDS, collaborate to build an AI-driven solution to make data collection and analysis more efficient. As a result, Silo AI built an assistant that finds and summarizes relevant paragraphs from the source data as well as combines numerical data from various sources into a single source. The pilot project indicates that such a solution based on Natural Language Processing (NLP) and automated data processing can save an estimated 30–40% of time spent on data analysis by individual experts.
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
Removing the background with an unmatched quality – How Kuvatilaus.fi and Silo AI use the latest methods in computer vision for flawless results.
Together with Tekniska Verken, we developed a robotics and computer vision-based solution that learns from each defect, and continuously improves the level of automation.
Canadian scenario generation company Riskthinking.AI and Silo AI co-created an AI solution to automatically extract climate risk data. We built a natural language processing-powered solution, that brings out insights related to one consequence of climate change: the rise of sea level.
In close collaboration with Helen, our experts developed an AI solution that predicts energy consumption levels based on historical data and local weather forecasts, reducing errors by a third.
Webinar recap: Computer vision for situational awareness.
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