Natan Mish
Lead Machine Learning Engineer at Zimmer Biomet. London School of Economics graduate with an MSc in Applied Social Data Science. Passionate about using Machine Learning to solve complicated problems. I have experience analysing, researching and building data products in the financial, real estate, transportation and healthcare industries. Curious about (almost) everything and always happy to take on new experiences and challenges. I love finding bugs, especially if they're my own making!
Sessions
This talk focuses on the benefits of using an event-driven approach for machine learning products. We will cover the basics of event-driven architecture for software development and provide examples of how it can be applied for machine learning use cases. The talk will be accompanied by live examples and code for you to follow along, using open source tools such as Apache Kafka, FastAPI and River. By the end of the talk, you'll have a good understanding of the advantages of event-driven architectures, such as improved scalability and responsiveness. If you are a machine learning practitioner interested in exploring this topic, this talk is a great starting point in which we will cover the concepts, tools and common pitfalls of the event driven framework for machine learning products.