3 key lessons Jungle's internship taught me
Last year, Jerónimo Mendes joined Jungle for a summer internship. Fast forward to the present and he’s officially one of Jungle’s software developers!
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Jungle to take part in Windpower Data and Digital Innovation Forum
The wind energy industry event will take place in Berlin, on March 7-8.
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Canopy deployed on two new wind farms in the Nordics
The latest additions to the Canopy portfolio are two wind farms in the Nordics, Kalax and Sørfjord, which are operated by European energy company Fortum.
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5 years, 5 lessons
This month, Jungle turned 5. During our journey of starting a deep-tech company in the Machine Learning space, we’ve grown as individuals and as a team, and learned many lessons. Today we’ll share the 5 most important ones with you.
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AI company Jungle raises EUR 5 million
Amsterdam & Lisbon, 5 September 2022 – Jungle, a Dutch artificial intelligence company, has closed a 5 million euro Series A funding round led by SHIFT Invest, complemented by Rocks International Group, EDP Ventures, Gorilla Growth Capital and Future Energy Ventures. This funding allows the company to accelerate the deployment of its technology in the renewable energy and industrial sectors.
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I’ve watched Jungle grow for the past 5 years
I’m Nelson and I’ve been a Software Engineer at Jungle for almost 5 years now. From intern, to Software Engineer, to team lead: here's my journey so far.
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Detecting silent failures in the making for our Heavy Industry customers
Our main product, Canopy, is built on top of very advanced AI models that are agnostic to the application. This means that they can be used to reduce machine downtime in various industries.
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Solving predictive maintenance challenges with Time-Machine AI models
Today we will go on a journey to discover some of the key advantages our Time-Machine AI models bring to predictive maintenance pipelines. They helped us solve some of the biggest challenges we have encountered on our way, so hold on to your seats!
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Unlocking scalable predictive analytics with next-gen AI models
The need for AI-based predictive solutions for electromechanical assets is on the rise. However, building large scale AI-dependent online systems is by no means an easy feat. Successfully creating useful prediction analytics pipelines requires experience and some of the big challenges are only uncovered once a solution is used during daily operations!
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