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
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.
Machine Learning w/ PostgresML
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.
Machine Learning @ LinkedIn
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.
Senior Technical Staff Member @IBM Research
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.
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.
AI Product Lead @Prefect
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