Having operated as a ML consultant over the last 5 years, and through my PhD before that, I have been exposed to a variety of problems and worked with professionals from both the academia as well as the industry across domains and departments. Currently operating as a Lead ML Engineer Vortexa Ltd.
From a Data Science and Research perspective, I am fascinated about the potential of Deep Learning and in particular its NLP applications as those are closer to my PhD research (click here for a copy of my PhD Thesis). I have also applied graph analytics extensively over the last 9 years—both for the purpose of my doctoral thesis, as well as for solving relevant problems for the many clients I have worked for.
From a software engineering perspective I am drawn to stream processing and relevant problems. I am an Apache Flink evangelist, which I have supported by running the respective meetup in London for 4 years.
Have a look at my repositories below.
With a PhD in Computer Science completed in 2014 and more than a decade of combined academic and industry experience under my belt, I have been on an engaging journey in the world of machine learning and AI. The last three years of this journey have been spent as a Lead Engineer at Vortexa Ltd, an AI-driven energy analytics company. Prior to this, I served as a Data Scientist and Data Engineer consultant at Data Reply Ltd, a leading provider of cutting-edge tech solutions. The mid-2010s, when AI re-emerged as a buzzword and was hailed as a magic solution for all business challenges, posed numerous obstacles. A recurring theme during this time was the inability to transition successful ML prototypes into production. The causes were numerous: A dearth of robust and scalable infrastructure capable of supporting ML models. An ever-widening gap between data scientists and software engineers, leading to misunderstood objectives and failed collaborations. Inadequate tools and methodologies to monitor, maintain, and improve models post-deployment. A general lack of understanding of the iterative nature of ML projects; a stark contrast to traditional software development. As these challenges became more apparent, the concept of the 'ML Engineer' began to take form. Though the term had been in circulation to some degree, it was around 2017 when it gained widespread recognition and began to define a new speciality within the industry. This role aimed to bridge the gap between data science and software engineering, focusing on implementing ML models into robust production environments. I quickly realized that my skill set and experience positioned me perfectly for this emerging role. As I embraced the 'ML Engineer' title, I embarked on an exciting new phase of my career. Now, I'm excited to share some of the valuable insights I've gleaned along the way.
I am very responsive on Linkedin. If you are looking to get in touch to discuss a topic of interest or something I have blogged about, please do so directly through my LinkedIn account. Alternatively, if you are looking for professional consulting services or if you would like me to evaluate your product, please e-mail me using the below template and use "Consultation Request" in the title of your message.
Hi! I am Christos; welcome to my website. “ML-Affairs” is the name and its sole purpose is to share my passion and learnings in this exciting area. If you share the same passion for applied data science and are looking for a refreshing look at things, then you are at the right place!