ML in Practice

We interact with large-scale machine learning systems on a daily basis. By powering our content feeds, securing our credit cards, detecting faces on our home cameras, and guiding our cars around traffic jams, we have come to rely on machine learning for our everyday needs. And while ML models are feted by academics, it is the ML infrastructure and tech stacks that productionalize those models at scale that make machine learning a practical reality.

In this track, we’ll look at the practical application of machine learning in experiences that you have come to rely on.


From this track

Session AI/ML

PostgresML: Leveraging Postgres as a Vector Database for AI

Thursday Jun 15 / 10:35AM EDT

With the growing importance of AI and machine learning in modern applications, data scientists and developers are constantly exploring new and efficient ways to store and analyze large amounts of data.

Speaker image - Montana Low

Montana Low

Machine Learning w/ PostgresML

Session Search

Needle in a 930M Member Haystack: People Search AI @LinkedIn

Thursday Jun 15 / 11:50AM EDT

LinkedIn's search functionality is one of its oldest capabilities, allowing members to search for people they know, or to discover new connections.

Speaker image - Mathew Teoh

Mathew Teoh

Machine Learning @ LinkedIn

Session AI/ML

Going Beyond the Case of Black Box AutoML

Thursday Jun 15 / 01:40PM EDT

Most AutoML tools are black-box tools. They offer no code/low code tools (UI/simple APIs) for practitioners to get started quickly. While this helps beginners, most experienced data scientists/ML practitioners often need more control.

Speaker image - Kiran Kate

Kiran Kate

Senior Technical Staff Member @IBM Research

Session ML in Practice

Back to Basics: Scalable, Portable ML in Pure SQL

Thursday Jun 15 / 02:55PM EDT

Redshift has SageMaker. BigQuery begat BigML. Spark birthed Databricks. Every data warehouse is tightly coupled to a particular ML stack.

Speaker image - Evan Miller

Evan Miller

Principal Statistics Engineer @Eppo (Creator of Evan's Awesome A/B Tools)


LLMs in the Real World: Structuring Text with Declarative NLP

Thursday Jun 15 / 04:10PM EDT

Building machine learning pipelines to extract structured data from unstructured text is a popular problem within an unpopular development lifecycle.

Speaker image - Adam Azzam

Adam Azzam

AI Product Lead @Prefect


Thursday Jun 15 / 10:30AM EDT


Track Host

Sid Anand

Chief Architect and Head of Engineering @Datazoom

Sid Anand currently serves as the Chief Architect and Head of Engineering for Datazoom, where he and his team build autonomous streaming data systems for Datazoom's high-fidelity, low latency streaming analytics needs. Prior to joining Datazoom, Sid served as PayPal's Chief Data Engineer, focusing on ways to realize the value of PayPal's hundreds of petabytes of data. Prior to joining PayPal, he held several positions including Agari's Data Architect, a Technical Lead in Search @ LinkedIn, Netflix’s Cloud Data Architect, Etsy’s VP of Engineering, and several technical roles at eBay. Sid earned his BS and MS degrees in CS from Cornell University, where he focused on Distributed Systems. Outside of work, Sid is a maintainer/committer on Apache Airflow and advises early-stage companies and several conferences (QCon, Data Council, and conferences under Skills Matter).

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