We are excited to announce the next SODA Social event at the Shoreditch Platform. On this occasion, our talks will be around combating challenges in Machine Learning and AI. Our confirmed speakers are Kostas Manolarakis from Stratagem, Kostas Perifanos from Argos and Stefan Boronea from Proportunity. As always, entrance is free plus we will be providing refreshments and nibbles. RSVP to book your space.
For more info contact [masked] or visit www.soda-social.com
We look forward to seeing you there.
Kostas Perifanos – Lead Machine Learning Engineer @ Argos
Kostas joined Argos in 2017 as a Lead Machine Learning engineer. Prior to Argos, he worked at Royal Mail, Mailonline, Pearson and in research; he was involved in a broad range of projects from European FP6 research programs to EdTech, Analytics, Search, Predictive Modelling using Machine Learning and AI. He is interested in Deep Learning, Distributed Computing, Optimisation, Search, Predictive Analytics and Natural Language Processing.
AI, Machine Learning, Ethics
Recent developments in AI & Machine Learning enable us to build machines with enough power to change society. The question is, do we, as human race, understand the implications of the technology revolution which is in progress?
Kostas Manolarakis - Lead Research and Development Engineer @ Stratagem Technologies
Kostas is a Quantitative Research Analyst and has extensive experience in academic research. He holds a bachelor degree in Mathematics, an MSc in Pure Mathematics from Cambridge University and a PhD in Numerical Stochastic Analysis from Imperial College London.
Is sports the next big thing for artificial intelligence? Artificial intelligence and machine learning is being adopted by more industries than ever before. A look at how the sports and particularly betting industries are driving this technology forward, with a focus on how machine learning is being utilised to extract key information from large data sets/
Stefan Boronea – CTO @ Proportunity
Stefan is the co-founder and CTO of Proportunity, a London startup using machine learning to understand and forecast real estate trends. Prior to starting Proportunity, he worked atBooking.com and IBM in both engineering and data science roles. He is fascinated by machine learning approaches to solving tangible problems, with a focus on predictive analytics, anomaly detection and emergent behaviour.
Machine learning at scale
Building machine learning models is tough. Building and testing machine learning models at scale is nearly impossible. How can we make deploying and testing models at scale easy, reliable and fast?