Petar Ilijevski
Currently solving the MLOps puzzle at Zalando, ensuring our pricing recommendation algorithms are as streamlined as my swimming technique. I spend my days shaping ML standards at scale and my free time training in the real world.
My life in two modes:
Running pipelines & Diving deep into infrastructure.
Running trails & Diving into the ocean.
Always looking to optimize the former to make more time for the latter."
Session
"Zero-copy" data transfer promises free communication between Spark's JVM and Python workers, but at 6 billion rows daily, the reality is far more complex. This session explores the low-level mechanics of distributed inference, focusing on the serialization bottlenecks that plague large-scale Gradient Boosted Tree models.
We will conduct a forensic analysis of execution plans generated by pandas_udf, mapInPandas, and SynapseML. By profiling memory hierarchies and CPU cycles, we visualize the true cost of pickling, Arrow record batching, and JNI context switching. Join this deep dive to understand the physics of distributed inference and learn how to tune spark.sql.execution.arrow.maxRecordsPerBatch to prevent OOMs without starving the CPU.