Presentation: ML's Hidden Tasks: A Checklist for Developers When Building ML Systems

When we started building the NLP infrastructure for a startup around 4 years ago, few people had put deep neural networks into production. As the only machine learning oriented person, I was tasked with the entire pipeline - from gathering data, to training the model, to deploying it, to convincing our client to trust it, to maintaining and improving the model over time. As developers, we have a check list of things that need to happen in our minds, including building the software, various types of testing, infrastructure, data management, DevOps, and improvements. Turns out machine learning has an entire set of unexpected things that go on that "take it to production" checklist. This talk will bring to light my story of how I learnt about the unexpected are, and what tools helped us through that. 

Audience:

• Software engineers who are moving into or currently working on machine learning systems
• CTOs who are busy bringing ML functionality to their tools and products and want to understand what to look at

What you can expect to learn:

• Unexpected details of putting models in production besides just the code, model and infrastructure: 
  • DataOps
  • Robustness and Uncertainty tests
  • Model Drift
  • Model testing approaches
  • Model Performance tracking
• Specific tools and technologies that will help address the unexpected details