WEBINAR REPLAY
| Audience: Architects and Developers
| Technical level: Intermediate
Operationalizing Machine Learning - Serving ML Models
With Boris Lublinsky, Principal Architect at Lightbend, Inc.
Join 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.
You will learn:
- 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
WEBINAR REPLAY
Operationalizing Machine Learning - Serving ML Models
With Boris Lublinsky, Principal Architect at Lightbend, Inc. and
Audience: Architects and Developers
Technical level: Intermediate
Join 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.
You will learn:
- 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
