Netflix (20240354585). OPTIMIZING DEEP LEARNING RECOMMENDER MODEL DATA PIPELINES WITH REINFORCEMENT LEARNING simplified abstract

OPTIMIZING DEEP LEARNING RECOMMENDER MODEL DATA PIPELINES WITH REINFORCEMENT LEARNING

Organization Name

netflix

Inventor(s)

Kabir Nagrecha of San Diego CA (US)

Lingyi Liu of Pleasanton CA (US)

Pablo A. Delgado Aqueveque of San Jose CA (US)

Prasanna Padmanabhan of San Jose CA (US)

OPTIMIZING DEEP LEARNING RECOMMENDER MODEL DATA PIPELINES WITH REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240354585 titled 'OPTIMIZING DEEP LEARNING RECOMMENDER MODEL DATA PIPELINES WITH REINFORCEMENT LEARNING

Simplified Explanation

The patent application discusses a method for using reinforcement learning to optimize data ingestion in a deep learning recommender model training pipeline.

• Reinforcement learning agent introduced into data ingestion pipeline
• Aims to reduce pipeline latency and memory usage
• Addresses issues like out-of-memory errors and poor responses to machine resizing

Key Features and Innovation

• Leveraging reinforcement learning in data ingestion pipeline
• Focus on reducing latency and memory usage
• Improving overall performance of deep learning recommender model training

Potential Applications

• Optimization of data ingestion in various deep learning applications
• Improving efficiency and performance of machine learning pipelines
• Enhancing resource allocation decisions in data processing systems

Problems Solved

• Out-of-memory errors in data ingestion pipelines
• Unoptimized user-defined functions
• Poor responses to machine resizing

Benefits

• Increased efficiency and performance of deep learning recommender models
• Reduction of pipeline latency and memory usage
• Enhanced resource allocation decisions in data processing systems

Commercial Applications

Optimizing data ingestion in deep learning applications can have significant commercial implications, particularly in industries relying on recommendation systems, such as e-commerce, streaming services, and personalized content platforms.

Questions about Reinforcement Learning in Data Ingestion

How does reinforcement learning improve data ingestion in deep learning pipelines?

Reinforcement learning helps the system make resource allocation choices that reduce latency and memory usage, leading to optimized performance.

What are the key benefits of using reinforcement learning in data ingestion pipelines?

The main benefits include improved efficiency, reduced errors, and better responses to system resizing, ultimately enhancing the overall performance of the deep learning recommender model.

Original Abstract Submitted

a computer-implemented method for leveraging reinforcement learning to optimize data ingestion in a deep learning recommender model training pipeline. for example, the discussed methods and systems introduce a reinforcement learning agent into a deep learning recommender model data ingestion pipeline to avoid many symptoms of an un-optimized data ingestion pipeline including, but not limited to, out-of-memory errors, un-optimized user-defined-functions in the data ingestion pipeline, and poor responses to machine re-sizing. the discussed methods and systems teach the reinforcement learning agent to make resource allocation choices within the data ingestion pipeline that are motivated by outcomes that reduce pipeline latency and memory usage. various other methods, systems, and computer-readable media are also disclosed.