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Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations.
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning.
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model.
Extending Relational Query Processing with ML Inference
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference.
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.
Helix: Accelerating Human-in-the-loop Machine Learning
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance.
How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems.
MLlib: Machine Learning in Apache Spark
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks.
