With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data and to train models. It can also be hard to scale with data sets that are increasingly frequently larger than the capacity of any single server. The size of the data can also make it hard to incrementally test and retrain models in near real-time to improve business results.
Attendees of this webinar learned how the Apache Ignite® in-memory computing platform addresses these machine learning limitations with distributed model training and execution to provide real-time, continuous learning capabilities. Topics include:
- Overview of distributed ML/DL including design, implementation, usage patterns, pros and cons
- Overview of Apache Ignite ML/DL, including prebuilt ML/DL, and how to add your own ML/DL algorithms
- How to integrate Apache Ignite with Apache Spark to improve the Apache Spark data pipeline throughput
- How Apache Ignite and TensorFlow can be used together to build distributed DL model training and execution
Attendees learned how distributed ML/DL works with Apache Ignite and how to get started. They also learned how Ignite can be used to create a continuous machine learning environment to drive real-time business processes.
Solution Architect at GridGain Systems