Operationalizing Machine Learning - Serving ML Models
A Look At Two Critical Aspects Of Machine Learning
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)
Continue Learning With These Resources
If you'd like to learn more, visit these Lightbend resources to keep up with this emerging competitive advantage:
- Read: Continue learning from Boris by grabbing his recent O'Reilly ebook Serving Machine Learning Models - A Guide to Architecture, Stream Processing Engines, and Frameworks
- Watch: Check out this related webinar "What's The Role Of Machine Learning In Fast Data And Streaming Applications?" by Lightbend Principal Data Scientist Emre Velipasaoglu.
- Learn: Download our Fast Data Platform technical overview to learn more about our easy-on ramp for designing, building, and running streaming and Fast Data applications.
As always, Lightbend is here to make your streaming, Fast Data, and Machine Learning journey successful. To find out more about our platform subscription, getting-started engagement services, or anything else, feel free to contact us below and schedule a 20-min introduction.