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


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.


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.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google