As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning in the Python Ecosystem, and we will cover the concepts that make production machine learning so challenging, as well as some of the recommended tools available to tackle these challenges.

This talk will cover key principles, patterns and frameworks around the open source frameworks powering single or multiple phases of the end-to-end ML lifecycle, incluing model training, deploying, monitoring, etc. We will be covering a high level overview of the production ML ecosystem and dive into best practices that have been abstracted from production use-cases of machine learning operations at scale, as well as how to leverage tools to that will allow us to deploy, explain, secure, monitor and scale production machine learning systems.

This talk will be relevant for any keen python practitioners or seasoned ML practitioners interested to get an updated overview of the state of the production ML ecosystem in the current year, covering a broad range of sub-fields in the space.

This talk will benefit the Python ecosystem by providing cross-functional knowledge, bringing together best practices from data scientists, software engineers and DevOps engineers to tackle the challenge of machine learning at scale. During this talk we will shed light into some of the more popular and up-and-coming libraries to watch in this space, and we will provide a conceptual and practical hands on deep dive which will allow the community to both, tackle this issues and help further the discussion.