Speaker: Brad Miro

Social media data is a mirror for our collective thoughts and views on the world, but working with these massive datasets is challenging. In this workshop, we’ll cover how Spark and Hadoop on Google Cloud can help us prepare datasets in parallel, at scale. Then we’ll use this data to build machine learning models with Spark MLlib, Google Cloud Natural Language API and Google Cloud AutoML. You'll walk away from this workshop with a better understanding of how to do natural language processing on massive troves of text data.

The introduction of deep learning into the data science toolkit has allowed for significant improvements on many important problems in data science. Many advancements in fields such as natural language processing, computer vision and generative modeling can be attributed to advancements in deep learning. In this talk, we will explain what deep learning is, why you may (or may not!) want to use it over traditional machine learning methods, as well as how to get started building deep learning models yourself using TensorFlow 2.0.  

Why do we want to specifically highlight TensorFlow 2.0? The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.0 version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a technique called transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.  

The TensorFlow ecosystem is rich with other offerings, and we would be remiss not to mention them. We will conclude by briefly discussing what these are, including Swift for TensorFlow, TensorFlow.js and TensorFlow Extended!

Throughout the day, we'll have speakers cover how they've adopted applied machine learning to software engineering. The day wraps with a discussion from the speakers on taking an applied, pragmatic approach to adding ML to your systems and how they solved challenges. Eager to deploy ML and have questions? This is a forum to discuss, learn, and help crystalize that roadmap. Join us discussing first principles adding ML to your systems.