AdiPolak
Adi is an experienced Software Engineer and people manager. For most of her professional life, she has worked with data and machine learning for operations and analytics. As a data practitioner, she developed algorithms to solve real-world problems using machine learning techniques and leveraging expertise in Apache Spark, Kafka, HDFS, and distributed large-scale systems.
Adi has taught Spark to thousands of students and is the author of the successful book — Scaling Machine Learning with Spark. Earlier this year, she embarked on a new adventure with data streaming, specifically Flink, and she can't get enough of it.
Designing a distributed system architecture can be a daunting task, with contradictory requirements and constraints constantly at play. The CAP theorem that directly states the challenges in distributed data stores presents a classic example where developers must choose between consistency, availability, and partition tolerance. The same applies to streaming infrastructure systems, where optimizing for one aspect can come at the cost of another. With cost, throughput, accuracy, and latency as the main constraints for streaming systems, it's crucial to make informed decisions that align with your business goals.
Designing a distributed system architecture can be a daunting task, with contradictory requirements and constraints constantly at play. The CAP theorem that directly states the challenges in distributed data stores presents a classic example where developers must choose between consistency, availability, and partition tolerance. The same applies to streaming infrastructure systems, where optimizing for one aspect can come at the cost of another. With cost, throughput, accuracy, and latency as the main constraints for streaming systems, it's crucial to make informed decisions that align with your business goals.
In the ever-evolving landscape of technology and Generative AI, integrating DevOps principles into the machine learning (ML) lifecycle is a transformative game-changer. Join me for an insightful session where we will explore essential aspects such as mlflow, deployment patterns, and monitoring techniques for ML models. Gain a deeper understanding of how to effectively navigate the complexities of deploying ML models into production environments. Discover best practices and proven strategies for monitoring and observing ML models in real-world scenarios.
Zero fluff.