Declarative Machine Learning Systems
https://arxiv.org/abs/2107.08148
Introduction
Machine learning: central in the strategy of tech companies and has gathered more attention from academia than ever before
Hastened by
Hardware improvements: enabled massive parallel processing
Data infrastructure improvements: enabled storage and consumption of massive datasets needed to train most ML models
Algorithmic improvements: better performance and scaling
Future envision: larger amount of people without skills to perform the same tasks. More declarative and abstract interfaces to hide complexity.
Describe
How current ML systems are structured
What factors are important for ML project success, which ones will determine wider ML adoption
What are the issues current ML systems are facing
How the systems being developed address them
What the next generation of ML systems look like
Software engineering meets ML
Distill common patterns that abstract the most mechanical parts of the process of building ML projects in a set of tools, systems, and platforms
The coming wave of ML systems
Substantial increases in adoption always come with separation of interests and hiding of complexity
Next-generation of ML systems that will allow people without ML expertise to train models and obtain predictions through more abstract interfaces
Machine Learning Systems
Challenges & desiderata:
Exponential decision explosion
Need: good defaults and automation
New model-itis
Need: standardization and focus on quality
Organizational chasms
Need: common interfaces
Scarcity of expertise
Need: higher level abstractions
Process slowness
Need: rapid iteration
Many diverse state
Need: separation of interests
Declarative ML systems
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