Architect’s Guide for Continuous Machine Learning Platforms With Apache Ignite 2.8
Many machine learning (ML) and deep learning (DL) platforms are slow in production environments. It can sometimes take hours or days to update ML models. This is a result of having the ML processing run on a different system from the operational transactions system in order to avoid a performance degradation.
Join us for this webinar to learn how to overcome these challenges by leveraging the Apache Ignite ML framework to implement a continuous machine learning (CML) platform. The CML platform can run the ML compute code on the same cluster that has the transactional data without having a performance impact on the transactions system. As a result, ML models can be updated in real-time using the latest available data.
Topics covered will include:
- An overview of massively distributed ML/DL architectures including design, implementation, usage patterns, and cost / benefit analysis
- Detailed coverage of Apache Ignite ML/DL Pipeline steps - from preprocessing to real-time prediction
- Discussion of out-of-the-box algorithms and adapters that can leverage third party algorithms such as Spark, XGBoost, TensorFlow, and custom code
- Detailed code examples and a demo that shows how to use Apache Ignite 2.8 ML framework for continuous learning tasks