What AI can learn from devops - a lesson on ML and Continuous Evaluation
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Thiago started in Pure Mathematics, jumped to predicting things and realized it was utterly dependent on understanding the inner bits of software development and data pipelines. Thus, his primary concern changed: how to bridge the gap between ML and production? :-) DataOps FTW! He is an active part of the community (devopsdays Amsterdam, ITNEXT & Codemotion), a knowledge-sharer, a proud father and a DataOps Lead. Thiago is pathologically curious and a continuous learner. He knows that high-performing data teams must decrease time-to-market and build production-ready applications, always!