Aditya Sakhuja is an Engineering Lead at Salesforce Einstein building ML products. He built the early prototype of a question answering system in salesforce’s ML journey and helped ship multiple ML products over the next few years in the service and collaboration space including article recommendations systems, case classification and working with external partners like Google. Earlier on he focused on pre-production performance and scalability analysis for distributed systems like msg queues and search gaining expertise around building and measuring low latency highly scalable systems. He has extensive knowledge in web/enterprise Search Systems, production deployed ML products along with offline and streaming data and serving systems. He got his Masters in CS from Georgia Institute of Technology and BS in Computer Engineering from the University of Pune, India.
November 17, 2020 04:00 PM PT
This talk will cover how we built and productionized automated machine learning pipelines at Salesforce. Starting with heuristics to automated retraining using technologies including but not limited to Scala, Python, Apache Spark, Docker, Sagemaker for training, and serving. We will walk through the generally applicable data prep, feature engineering, training, evaluation/comparisons, and continuous model training including data feedback loops in containerized environments with Sagemaker. We will talk about our deployment and validation approach. Finally, we’ll draw lessons from iteratively building an enterprise ML product. Attendees will learn about the mental models for building end to end prod ML pipelines and GA ready products.
Speaker: Aditya Sakhuja