An upcoming regular event for people interested in Big Data, whether you are professionals in the field, enthusiasts, or students looking to build your career in that direction. We'll host interesting talks from industry experts, a focus on both business and technical aspects of big data, and plenty of time for networking! Big Data, Stockholm is hosted and managed by Dataconomy Media GmbH. Please get in touch with firstname.lastname@example.org if you are interested in participating.
Join us for an evening of exciting talks from Data Science Industry leaders and experts, followed by enough time for a few drinks, nibbles and networking.
6:00 - 6:15 PM: Registration
6:15 - 6:30 PM: Paula Grohmann, Head of Events at Data Natives
'Welcome to Data Natives!'
6:35 - 6:55 PM: Marc Weimer-Hablitzel, Principal Data Solutions at etventure & Lead Data Hub at Gruenderallianz
"Startups: Lifeguards of the corporate data lake"
Are the corporate data lakes the death traps of data science? And can startups be the lifeguards of these corporate date lakes?
7:00 - 7:20 PM: Yury Babak, The head of ML/DL framework development at GridGain
"Distributed Machine and Deep Learning at Scale with Apache Ignite"
With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data for ETL, and hours to train models. It's also hard to scale, with data sets increasingly being larger than the capacity of any single server. The amount of the data also makes it hard to incrementally test and retrain models in near real-time.
7:25 - 7:35 PM: Andreas Hellander, CSO at Scaleout
"Federated Machine Learning for Collaborative and Secure AI"
Federated Machine Learning (FedML) is a distributed machine learning approach which enables training on decentralised data. A server coordinates a network of nodes, each of which has local, private training data. The nodes contribute to the construction of a global model by training on local data , and the server combines non-sensitive node model contributions into the global model. Federated learning addresses fundamental problems of centralized AI such as privacy, ownership, and locality of data. It extends, even disrupts, the centralized AI paradigm in which better algorithms always comes at the cost of collecting more and more sensitive data.
7:35 - 7:45 PM: TBA
7:50 - 8:45 PM: Food and Drinks
If you would like to get in touch with us for getting involved, please write us an email: [masked]. Looking forward to seeing you there for another night of great talks, a few drinks, food and some networking!
The event will be held in English.
The DN Team