Machine Learning in Distributed Systems

Data is growing in variety, velocity and volume every year and COVID definitely helped on that. Supply of Infrastructure is also growing providing us with more compute to train bigger and more complex models using all this available data. And everything comes to one question….

How long are you willing to wait for the model to be trained?

World is moving fast, requirements change quickly but we still find ourselves waiting ages for the training process to finish. Leveraging new technologies and different ways to optimize the model training is key to create production level models quickly, iterate faster and have more business impact. Spark with its distributing power can help us with that!

Are you ready to push some boundaries in the model training?

Programming & Development

About the speaker

Maria Zervou

Maria has worked as a Data Scientist and an ML engineer for the past 4 years. She is now working as a Senior SSA @Databricks where she helps businesses leverage data and AI to solve their data challenges at scale!

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Discussion 

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