Scaling up machine-learning (ML), data retrieval and reasoning algorithms from Artificial Intelligence (AI) for massive datasets is a major technical challenge in our time. The scaling process can also have different dimensions: performance, development productivity, number of employees…
In this talk I will showcase how we used to develop Machine learning features at GitHub, the pain points we had and how we changed our infrastructure and way of development in order to productionize multiple ML features in terms of hours/days.
In addition, I will explore with the audience the main factors I consider when scaling ML at medium to big companies.
By the end of the talk you should have an overview and applicable framework on how to help scaling ML processes in your company.
Jose David Baena is a Senior Software Engineer at GitHub. He has more than 10 years experience in backend development, from startups to big companies, from Europe to the United States.
His experience ranges from building distributed low latency systems for financial companies to high performant crawlers for social media.
At the moment, he designs architectures that are used by the Machine Learning and Data Science teams at GitHub. He is passionate about distributed systems, machine learning scalability and developer productivity.