Scaling Production ML Pipelines with Databand
Scaling Production ML Pipelines with Databand

Abstract: 

In this session, we will discuss how Databand is helping data engineering teams productize and scale machine learning pipelines. We will present a real-world use case in the retail technology space but will describe how the underlying functionality is generally applicable for any data/ML engineering team. The talk will cover a typical day of running large-scale scheduled ML data prep & training processes, monitoring for issues in jobs and data quality, managing ML-specific alerts, and faster debugging. Attendees will learn how Databand layers onto production systems to provide best-in-breed monitoring for the ML lifecycle.

Bio: 

Josh Benamram is Co-Founder of Databand, an APM and observability solution for data engineering teams. Josh comes from a background in Product Management and VC in the data infrastructure space. Prior to Databand he was a Product Manager at Sisense, a business analytics platform provider, where he led Sisense’s database, ETL, and predictive analytics product components. Before Sisense, Josh was a VC investor at Bessemer Venture Partners, focusing on machine learning and data infrastructure investments.