How to use continual learning in your ML models

#artificialintelligence

Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. CL is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. CL of models in production will improve accuracy, and bring AI one step closer to real human intelligence.


Continual Learning of Models in Production Open Data Science Conference

#artificialintelligence

Abstract: Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. CL of models in production will improve accuracy, and bring artificial intelligence one step closer to real human intelligence.


Online Workshop: How to set up Kubernetes for all your machine learning workflows

#artificialintelligence

The goal of data science teams are to build and deploy high impact models. Data scientists prefer to focus on building algorithms, while data engineers focus on performance and productionizing machine learning. Kubernetes is an orchestration platform that can be deployed anywhere and can serve any kind of machine and deep learning environment. Kubernetes is a great tool for data scientists to use to stay productive and for data engineers to get production-ready results. In this free workshop you'll learn how to build your own Kubernetes to use in your next machine learning pipeline.


Data Science: What to Expect in 2019

#artificialintelligence

Data science is rapidly changing. New advances in AI and machine learning mean that data can be applied in brand new ways, and in unprecedented modeling systems, to do much more than was possible just a few years ago. The cloud is also ushering in a new era of data science by making software more portable and versatile. Techopedia asked the experts what we might see in the year ahead. Here's some of what's likely to come our way in 2019.


SeeTree raises $11.5M to help farmers manage their orchards

#artificialintelligence

SeeTree, a Tel Aviv-based startup that uses drones and artificial intelligence to bring precision agriculture to their groves, today announced that it has raised an $11.5 million Series A funding round led by Hanaco Ventures, with participation from previous investors Canaan Partners Israel, Uri Levine and his investors group, iAngel and Mindset. This brings the company's total funding to $15 million. The idea behind the company, which also has offices in California and Brazil, is that in the past, drone-based precision agriculture hasn't really lived up to its promise and didn't work all that well for permanent crops like fruit trees. "In the past two decades, since the concept was born, the application of it, as well as measuring techniques, has seen limited success -- especially in the permanent-crop sector," said SeeTree CEO Israel Talpaz. "They failed to reach the full potential of precision agriculture as it is meant to be."