Helix

Xin, Doris; Macke, Stephen; Ma, Litian; Liu, Jialin; Song, Shuchen; Parameswaran, Aditya

doi:10.14778/3297753.3297763

Cited by 40 publications

(5 citation statements)

References 59 publications

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“…To motivate the challenges unique to IoT settings we present several example IoT applications and their data sources. We then identify the unique challenges for ML systems in IoT settings, which existing general purpose training [17,63,66] and serving [14,51] systems fall short on.…”

Section: Applications and Challengesmentioning

confidence: 99%

“…Adapting to Changes in Environmental Context: Existing ML systems [5,14,51,66,67] often rely on generalized ML models, which are infeasible in IoT settings since each environment is unique and models needs to be contextualized to that environment. Furthermore, IoT systems are also affected by changes in the ambient environment, both temporary or permanent (e.g.…”

Section: Challenges For ML Systems In Iot Settingsmentioning

confidence: 99%

“…Most of the training focused systems [11,17,63,66] optimize for deep neural network models with many hyperparameters, which is very resource and time intensive. Project Adam [11] investigates distributed training based on available resources while Helix [66] and KeystoneML [63] use various techniques to optimizes the ML training workflow.…”

Section: Large-scale ML Training Systemsmentioning

confidence: 99%

“…The holy grail is to "train" a generalized ML model once and then "serve" or deploy it to make predictions. In fact, several general-purpose ML serving-only systems exist, [5,14,51,66,67] including those for IoT relevant audio data [38] or image sources. These systems relying on pre-trained models do not work well for IoT settings for several reasons.…”

Section: Introductionmentioning

confidence: 99%

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MLIoT

Boovaraghavan

Maravi

Mallela

et al. 2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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Modern Internet of Things (IoT) applications, from contextual sensing to voice assistants, rely on ML-based training and serving systems using pre-trained models to render predictions. However, real-world IoT environments are diverse, with rich IoT sensors and need ML models to be personalized for each setting using relatively less training data. Most existing general-purpose ML systems are optimized for specific and dedicated hardware resources and do not adapt to changing resources and different IoT application requirements. To address this gap, we propose MLIoT, an end-to-end Machine Learning System tailored towards supporting the entire lifecycle of IoT applications. MLIoT adapts to different IoT data sources, IoT tasks, and compute resources by automatically training, optimizing, and serving models based on expressive applicationspecific policies. MLIoT also adapts to changes in IoT environments or compute resources by enabling re-training, and updating models served on the fly while maintaining accuracy and performance. Our evaluation across a set of benchmarks show that MLIoT can handle multiple IoT tasks, each with individual requirements, in a scalable manner while maintaining high accuracy and performance. We compare MLIoT with two state-of-the-art hand-tuned systems and a commercial ML system showing that MLIoT improves accuracy from 50% -75% while reducing or maintaining latency. CCS CONCEPTS• Computer systems organization → Client-server architectures; • Computing methodologies → Artificial intelligence.

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Section: Applications and Challengesmentioning

confidence: 99%

Section: Challenges For ML Systems In Iot Settingsmentioning

confidence: 99%

Section: Large-scale ML Training Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MLIoT

Boovaraghavan

Maravi

Mallela

et al. 2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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“…There is active research on interactive and human-in-the-loop systems in many computer science disciplines. The database and visualization communities have produced numerous tools [3][4][5][6][7][8] to aid data scientists with data wrangling and analysis. At the decision-making stage, the machine learning community has looked at making black box models explainable [2,[9][10][11][12], while the human-computer interaction (HCI) community has been studying how differences in explainability affect decision making [13,14].…”

Section: Prior Workmentioning

confidence: 99%

Amplifying Domain Expertise in Clinical Data Pipelines (Preprint)

Rahman¹,

Nandi²,

Hebert³

2020

Preprint

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UNSTRUCTURED Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

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Formal semantics and high performance in declarative machine learning using Datalog

Wang

et al. 2021

The VLDB Journal

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With an escalating arms race to adopt machine learning (ML) in diverse application domains, there is an urgent need to support declarative machine learning over distributed data platforms. Toward this goal, a new framework is needed where users can specify ML tasks in a manner where programming is decoupled from the underlying algorithmic and system concerns. In this paper, we argue that declarative abstractions based on Datalog are natural fits for machine learning and propose a purely declarative ML framework with a Datalog query interface. We show that using aggregates in recursive Datalog programs entails a concise expression of ML applications, while providing a strictly declarative formal semantics. This is achieved by introducing simple conditions under which the semantics of recursive programs is guaranteed to be equivalent to that of aggregate-stratified ones. We further provide specialized compilation and planning techniques for semi-naive fixpoint computation in the presence of aggregates and optimization strategies that are effective on diverse recursive programs and distributed data platforms. To test and demonstrate these research advances, we have developed a powerful and user-friendly system on top of Apache Spark. Extensive evaluations on large-scale datasets illustrate that this approach will achieve promising performance gains while improving both programming flexibility and ease of development and deployment for ML applications.

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Helix

Cited by 40 publications

References 59 publications

MLIoT

MLIoT

Amplifying Domain Expertise in Clinical Data Pipelines (Preprint)

Formal semantics and high performance in declarative machine learning using Datalog

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