Automating Machine Learning and Deep Learning Workflows

Mourafiq: This talk is going to be about how to automate machine learning and deep learning workflows and processes. Before I start, I will talk a bit about myself. My name is Mourad [Mourafiq], I have a background in computer science and applied mathematics. I've been involved and working in the tech industry and the banking industry for the last eight years, and I've been involved in different roles involving mathematical modeling, software engineering, data analytics, data science. For the last two years, I've been working on a platform to automate and I manage the whole life cycle of machine learning and the model management, called Polyaxon. Since I will be talking about a lot of processes and best practices and ideas to basically streamline your model managements at work, I'll be referring a lot to Polyaxon as an example of a tool for doing these data science workflows. Several approaches and solutions are based on my own experience developing this tool, and talking with customers and the community users since the platform is open source. Polyaxon is a platform that tries to solve the machine learning life cycle. Basically, it tries to automate as much as possible so that you can iterate as fast as possible on your model production and model deployments. It has a no lock-in feature.