Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.
- Be introduced to machine learning, Spark, and Spark MLlib 2.4.x
- Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries
- Detect anomalies with the Isolation Forest algorithm for Spark
- Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages
- Optimize your ML workload with the Alluxio in-memory data accelerator for Spark
- Use GraphX and GraphFrames for Graph Analysis
- Perform image recognition using convolutional neural networks
- Utilize the Keras framework and distributed deep learning libraries with Spark
Who This Book Is For
Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.