Machine Learning on Distributed Systems
Most real-world data science workflows require more than multiple cores on a single server to meet scale and speed demands, but there is a general lack of understanding when it comes to what machine learning on distributed systems looks like in practice. Gartner and Forrester do not consider distributed execution when they score advanced analytics software solutions. Many formal machine learning training occurs on single node machines with non-distributed algorithms. In this talk we will discuss why an understanding of distributed architectures is important for anyone in the analytical sciences. We will cover the current distributed machine learning ecosystem. We will review common pitfalls when performing machine learning at scale.
We will discuss architectural considerations for a machine learning program, and introduce a methodology for choosing the appropriate machine learning architecture for a given use case.
HPE - Senior Data Scientist
Josh Poduska is a Senior Data Scientist in HPE’s Big Data Software Group. Josh has 16 years of experience as a practitioner in the analytical sciences with an emphasis on machine learning and statistical applications. He spent the last six years focusing on advanced analytical solutions with MPP columnar databases. At HPE he is part of the Vertica team and enjoys helping organizations solve their toughest data challenges.
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