Everyone loves talking about successes, but data science projects fail all the time. Datasets don’t end up having signals, the work takes far longer than expected, and products end up missing the mark. In this recorded talk I examine the key themes that show up in projects that fail and how data scientists can spot them coming. To highlight these themes I use examples from the many failed data science projects that I have been personally responsible for.
Deep learning sounds complicated and difficult, but it’s really not. Thanks to packages like Keras, you can get started with only a few lines of R code. Once you understand the basic concepts, you will able to use deep learning to make AI-generated humorous content! In this talk I give an introduction to deep learning by showing how you can use it to make a model that generates weird pet names like: Shurper, Tunkin Pike, and Jack Odins. If you understand how to make a linear regression in R, you can understand how to create fun deep learning projects.
When tasked with creating the first customer-facing machine learning model at T-Mobile we had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. In this talk, we walk through how to deploy R models as container-based APIs and the struggles and triumphs we've had using R in production.