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Operationalizing Machine Learning - Serving ML Models

A Look At Two Critical Aspects Of Machine Learning

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In this webinar, we are joined by O’Reilly author and Lightbend Principal Architect, Boris Lublinsky, as he discusses one of the hottest topics in software engineering today: serving machine learning models.

Typically with machine learning, different groups are responsible for model training and model serving. Data scientists often introduce their own machine-learning tools, causing software engineers to create complementary model-serving frameworks to keep pace. It’s not a very efficient system. In this webinar, Boris demonstrates a more standardized approach to model serving and model scoring, focusing on:

  • How to develop an architecture for serving models in real time as part of input stream processing
  • How this approach enables data science teams to update models without restarting existing applications
  • Different ways to build this model-scoring solution, using several popular stream processing engines and frameworks

Watch The Full Video (~40 Min)

Watch on YouTube

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