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      How Observability Uncovers the Effects of ML Technical Debt
      Data in Deployment

      How Observability Uncovers the Effects of ML Technical Debt

      One of the most alarming aspects of machine learning is that many teams don’t yet have tools and processes to measure the negative effects of technical debt in their production systems. Many teams test their machine learning models offline but conduct little to no online evaluation after initial deployment. These teams are flying blind—running production… Continue reading How Observability Uncovers the Effects of ML Technical Debt

      Bernease Herman
      Danny D. Leybzon
      +1
      Bernease Herman, Danny D. Leybzon, Alessya Visnjic
      Whose Ground Truth? Accounting for Individual and Collective Identities…
      Labeling and Crowdsourcing

      Whose Ground Truth? Accounting for Individual and Collective Identities…

      Emily Denton, Mark Diaz, Ian D Kivlichan, Vinodkumar Prabhakaran, Rachel Rosen Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets has not received nearly enough attention. In this paper, we survey an array of literature that… Continue reading Whose Ground Truth? Accounting for Individual and Collective Identities…

      Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution
      Data in Deployment

      Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution

      Wenpeng Yin, Shelby Heinecke, Jia LiNitish Shirish Keskar, Michael Jones, Shouzhong ShiStanislav Georgiev, Kurt Milich, Joseph Esposito, Caiming Xiong The distribution gap between training datasets and data encountered in production is well acknowledged. Training datasets are often constructed over a fixed period of time and by carefully curating the data to be labeled. Thus, training… Continue reading Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution

      AutoDC: Automated data-centric processing
      Data in Deployment

      AutoDC: Automated data-centric processing

      Zac Yung-Chun Liu, Shoumik Roychowdhury, Scott TarlowAkash Nair, Shweta Badhe, Tejas Shah AutoML (automated machine learning) has been extensively developed in the past few years for the model-centric approach. As for the data-centric approach, the processes to improve the dataset, such as fixing incorrect labels, adding examples that represent edge cases, and applying data augmentation,… Continue reading AutoDC: Automated data-centric processing

      Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
      Data in Deployment

      Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation

      Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan As the adoption of deep learning techniques in industrial applications grows with increasing speed and scale, successful deployment of deep learning models often hinges on the availability, volume, and quality of annotated data. In this paper, we tackle the problems of efficient data labeling and annotation verification… Continue reading Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation

      Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
      Labeling and Crowdsourcing

      Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation

      Saahil Jain, Akshay Smit, Andrew Ng, Pranav Rajpurkar Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report labeling on the performance of chest X-ray classification models has not been systematically investigated. We first compare the CheXpert, CheXbert, and VisualCheXbert… Continue reading Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation

      Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
      Labeling and Crowdsourcing

      Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification

      Heng Hao, Hankyu Moon, Sima Didari, Jae Oh Woo, Patrick Bangert We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neural Network and (2) the Gaussian Process (GP) method, in sequence to achieve highly data and label efficient classifications. Moreover, both elements… Continue reading Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification

      Single-Click 3D Object Annotation on LiDAR Point Clouds
      Labeling and Crowdsourcing

      Single-Click 3D Object Annotation on LiDAR Point Clouds

      Trung Nguyen, Binh-Son Hua, Duc Thanh Nguyen, Dinh Phung We present a simple and effective tool for performing interactive 3D object annotation for 3D object detection on LiDAR point clouds. Our annotation pipeline begins with a pre-labeling stage that infers 3D bounding boxes automatically by using a pre-trained deep neural network. While this stage can… Continue reading Single-Click 3D Object Annotation on LiDAR Point Clouds

      Ontolabeling: Re-Thinking Data Labeling For Computer Vision
      Labeling and Crowdsourcing

      Ontolabeling: Re-Thinking Data Labeling For Computer Vision

      Nicola Croce, Marcos Nieto Over the last decade, developments in computer vision tasks have been driven by image, video, and multimodal benchmark datasets fueling the growth of machine learning methods for object detection, classification, and scene understanding. Such advances have, however, created static, goal-specific and heterogeneous datasets, with little to none emphasis on the used… Continue reading Ontolabeling: Re-Thinking Data Labeling For Computer Vision

      Contrasting the Profiles of Easy and Hard Observations in a Dataset
      Data in Deployment

      Contrasting the Profiles of Easy and Hard Observations in a Dataset

      Camila Castro Moreno, Pedro Yuri Arbs Paiva, Gustavo H. Nunes, Ana Carolina Lorena For supporting data-centric analyzes, it is important to identify and characterize which observations from a dataset are hard or easy to classify. This paper employs meta-learning strategies to describe the main differences between observations which are easy and hard to classify in… Continue reading Contrasting the Profiles of Easy and Hard Observations in a Dataset

      What can Data-Centric AI Learn from Data and ML Engineering?
      Data in Deployment

      What can Data-Centric AI Learn from Data and ML Engineering?

      Alkis Polyzotis, Matei Zaharia Data-centric AI is a new and exciting research topic in the AI community, but many organizations already build and maintain various data-centric” applications whose goal is to produce high quality data. These range from traditional business data processing applications (e.g.,how much should we charge each of our customers this month?”) to… Continue reading What can Data-Centric AI Learn from Data and ML Engineering?

      Challenges and Solutions to build a Data Pipeline to Identify Anomalies in Enterprise System Performance
      Data in Deployment

      Challenges and Solutions to build a Data Pipeline to Identify Anomalies in Enterprise System Performance

      Xiaobo Huang, Amitabha Banerjee, Chien-Chia Chen, Chengzhi Huang, Tzu Yi Chuang, Abhishek Srivastava, Razvan Cheveresan, We discuss how VMware is solving the following challenges to harness data to operate our ML-based anomaly detection system to detect performance issues in our Software Defined Data Center (SDDC) enterprise deployments: (i) label scarcity and label bias due to… Continue reading Challenges and Solutions to build a Data Pipeline to Identify Anomalies in Enterprise System Performance

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