Learn with Silo AI

The latest articles, webinars, podcasts and more for you to learn how you can benefit from artificial intelligence.
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Physical AI Systems — AI with a body

An embodied AI system can actively perceive the world around them in order to gather information through sensors such as vision, process this information on different cognitive levels, both autonomously and in interaction with a human, and finally make plans or decisions based on the outcome. In the autonomous vehicle case, the internal processing consists of planning future driving actions in interaction with the surrounding world and possibly also with a driver.

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Implementing end-to-end scalable MLOps for a computer vision product

The overall goal of MLOps is to make the process of productizing ML models smoother. MLOps applies the DevOps techniques, concepts and practices to machine learning systems with an increased demand to take care of aspects around the activities, such as data versioning, data lineage and data quality. MLOps responses to an increased need for model observability and monitoring model performance.

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MLOps is not a platform, it’s a human process assisted by platforms

Machine learning operations, MLOps, is the organizational practice for operationalizing AI and accelerating ML development in a sustainable way. Once you’ve validated a couple of AI use cases by pilots and proofs-of-concept, scaling the development and the use of AI in your organization will require both a solid machine learning infrastructure and an aligned way of operating.

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Rolls of paper at Kemira

Kemira and Silo AI leverage machine learning to predict and prevent process disturbances and quality issues in paper and board production

The future of the papermaking process is data-driven. This can already be seen today in the collaboration between Kemira, the global chemicals company serving customers in the pulp and paper industry, and Silo AI, the largest private AI lab in the Nordics. The two companies are today announcing their collaboration, which has already resulted in a predictive AI-driven solution taken into production in board and paper production.

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Kai Knuutila joins Silo AI to empower industrial companies with edge AI

We’re excited to get Kai Knuutila join our team as a Lead AI Solutions Strategist. Kai is a long-term digitalization and technology professional, with experience in leading teams in multiple technology areas ranging from software, hardware, semiconductors to IoT, wearables and AI. With a solid work experience of 22+ years, of which 17 spent at two technology giants Intel and Nokia, Kai is an experienced senior technology strategist and advisor.

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Teppo kuisma intro blog

Teppo Kuisma takes the lead of Silo AI’s international growth

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.

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