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model-interpretability

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kserve / kserve

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isvc default resource requests/limits should be optional/configurable via ConfigMap

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adamkgray
adamkgray commented May 4, 2021

/kind feature

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In pkg/apis/serving/v1beta1/inference_service_defaults.go the default InferenceService resource requests and limits are hard coded to be 1 cpu and 2Gi memory. These are reasonable defaults. However, the entire existence of these defaults should be disablable. Moreover, administrators should be able to quickly adjust defaults globally via t

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good first issue kind/feature kfserving/control-plane
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rehmanzafar / xai-iml-sota

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Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

machine-learning human-in-the-loop interpretability explainable-artificial-intelligence researchers interactive-machine-learning deep-learning-visualization human-in-the-loop-machine-learning explainable-ml xai interpretable interpretable-machine-learning iml model-interpretability explainable interpretable-models explainable-models interpretable-learning explaining-ai explanation-methods
  • Updated Jun 18, 2021
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Ankit-Kumar-Saini / Coursera_TensorFlow_Advanced_Techniques_Specialization

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Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

object-detection image-segmentation generative-adversarial-networks auto-encoders saliency-map mask-rcnn variational-autoencoders coursera-specialization retina-net model-interpretability tensorflow2 functional-api custom-model-development custom-loss-functions gradcam-visualization custom-layers gradient-tape-optimization custom-training-loops
  • Updated Jun 9, 2021
  • Jupyter Notebook

amascasadesus / ML-for-Paediatric-Covid-Diagnosis

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A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.

python data-science machine-learning random-forest pipelines hyperparameter-optimization data-preprocessing data-cleaning decision-tree hyperparameter-tuning shapely smote oversampling classification-models model-interpretability classification-algorithms missing-data-imputation model-agnostic-explanations model-optimization global-surrogate-method
  • Updated Jul 21, 2020
  • Jupyter Notebook

Shuyib / chronic-kidney-disease-kaggle

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Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

data-science machine-learning machine-learning-algorithms data-transformation data-visualization feature-selection dimensionality-reduction diagnostics feature-engineering health-data-analysis machine-learning-algorithm model-interpretability data-cleaning-pipeline health-data-science preventative-medicine
  • Updated Aug 24, 2021
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