Declarative machine learning systems

@article{Molino2022DeclarativeML,
  title={Declarative machine learning systems},
  author={Piero Molino and Christopher R'e},
  journal={Communications of the ACM},
  year={2022},
  volume={65},
  pages={42 - 49}
}
The future of machine learning will depend on it being in the hands of the rest of us. 
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References

SHOWING 1-10 OF 30 REFERENCES

The hardware lottery

After decades of incentivizing the isolation of hardware, software, and algorithm development, the catalysts for closer collaboration are changing the paradigm.

Hidden Technical Debt in Machine Learning Systems

TLDR
It is found it is common to incur massive ongoing maintenance costs in real-world ML systems, and several ML-specific risk factors to account for in system design are explored.

Real-World Learning with Markov Logic Networks

TLDR
Application to a real-world university domain shows the Markov logic networks approach to be accurate, efficient, and less labor-intensive than traditional ones.

MLlib: Machine Learning in Apache Spark

TLDR
MLlib is presented, Spark's open-source distributed machine learning library that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.

DeepDive: Declarative Knowledge Base Construction

TLDR
DeepDive is described, a system that combines database and machine learning ideas to help develop KBC systems, a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration.

Neural Architecture Search: A Survey

TLDR
An overview of existing work in this field of research is provided and neural architecture search methods are categorized according to three dimensions: search space, search strategy, and performance estimation strategy.

Overton: A Data System for Monitoring and Improving Machine-Learned Products

TLDR
Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions that shift developers to these higher-level tasks instead of lower-level machine learning tasks.

DeepDive

TLDR
DeepDive is described, a system that combines database and machine learning ideas to help to develop KBC systems, to frame traditional extract-transform-load (ETL) style data management problems as a single large statistical inference task that is declaratively defined by the user.

SQLFlow: A Bridge between SQL and Machine Learning

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe…

Ludwig: a type-based declarative deep learning toolbox

TLDR
Ludwig is a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code, and introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks.