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Whitepaper

Pipelining Machine Learning Models Together

Pipelining is the art of splitting up your machine learning workflows into reusable, modular parts in order to build more powerful software over time.

What you'll learn about pipelining in this whitepaper:

Pipelining Machine Learning Models Together

Pipelining is the art of splitting up your machine learning workflows into reusable, modular parts in order to build more powerful software over time.

Pipelining

What you'll learn about pipelining in this whitepaper:

  • The ways that pipelining can have a large impact on performance and design of your organization's workflows and how scaling is difficult in a monolithic architecture
  • Three major benefits of pipelining in ML projects: volume, versioning, and variety of tasks
  • How pipelining works in a sentiment analysis use case using Twitter data to discern customer satisfaction