Streaming, reactive, real-time machine learning

Real-time, reactive data processing and streaming machine learning: In today's episode, the positively brilliant researcher and entrepreneur Adrian Kosowski, PhD fills us in on what the future of ML will be like.

Adrian:
• Is Co-Founder and Chief Product Officer at Pathway, a framework for real-time, reactive data processing that is based in Paris.
• Has over 15 years of research experience, including 9 years at Inria (a prestigious French computer science center), leading to the co-authorship of over 100 articles in a range of fields (theoretical computer science, physics, and biology) covering topics like network science, distributed algorithms and complex systems.
• Previously co-founded and led business development for Spoj.com, a competitive programming platform used by millions of software developers.
• Obtained his PhD in Computer Science at the ripe old age of 20.

Adrian has also generously offered to ship a Pathway hoodie (to anywhere in the world!) to the first ten commenters on this post who request one!

Today’s episode will appeal primarily to hands-on practitioners like data scientists, ML engineers, and data engineers. However, we do our best to break down technical terms and provide concrete examples of topics so that anyone can enjoy learning about the cutting edge in training ML models.

In this episode, Adrian details:
• What streaming data processing is and why it’s superior in many ways to the batch training of ML models that historically dominated data science.
• How streaming data processing allows efficient, real-time model training.
• How reactive data processing enables data applications to react instantly and automatically to never-before-seen input data potentially saving firms vast sums.
• When a computer scientist should become a product leader.
• What programming languages Pathway selected for their platform & why.
• The big up-and-coming opportunity for data and ML start-ups.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.