2-day training courses

Brooke Wenig walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs. Read more.

To handle real-time big data, you need to solve two difficult problems: how do you ingest that much data and how will you process that much data? Jesse Anderson explores the latest real-time frameworks (both open source and managed cloud services), discusses the leading cloud providers, and explains how to choose the right one for your company. Read more.

The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs, making it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll demonstrates TensorFlow's capabilities and walks you through building machine learning models on real-world data. Read more.

Brian Bloechle demonstrates how to implement typical data science workflows using Apache Spark. You'll learn how to wrangle and explore data using Spark SQL DataFrames and how to build, evaluate, and tune machine learning models using Spark MLlib. Read more.

Angie Ma offers a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization. Read more.

PyTorch is a recent deep learning framework from Facebook that is gaining massive momentum in the deep learning community. Its fundamentally flexible design makes building and debugging models straightforward, simple, and fun. Delip Rao and Brian McMahan walk you through PyTorch's capabilities and demonstrate how to use PyTorch to build deep learning models and apply them to real-world problems. Read more.

Zachary Glassman demonstrates how to build intelligent business applications using machine learning, taking you through each step in developing a machine learning pipeline, from prototyping to production. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend your knowledge by building two applications from real-world datasets. Read more.