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python machine-learning data-preprocessing tensorflow-transform mlops

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comet mlflow mlops

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apache-spark pyspark scikit-learn mlops

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python flask mqtt twisted

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machine-learning version-control mlops dvc

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azure azure-devops azureml mlops

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apache-spark pyspark scikit-learn mlflow mlops

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azure azure-devops azure-pipelines-build-task mlops

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amazon-web-services amazon-sagemaker mlops

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python scikit-learn mlflow mlops

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azure gcc pip ruamel.yaml

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git sparse-checkout git-sparse-checkout

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python azure mlflow mlops

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python mlflow kedro mlops

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python kedro mlops

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python google-kubernetes-engine dask prefect mlops

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docker tensorflow object-detection tensorflow-serving mlops

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python amazon-web-services amazon-sagemaker aws-codebuild

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javascript arrays json

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azure docker azure-web-app-service azure-container-instances azure-container-registry

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machine-learning data-science mlops

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python google-cloud-platform google-cloud-vertex-ai mlops

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mlops tritonserver

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amazon-s3 minio mlflow mlops

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azure machine-learning azure-devops azureml mlops

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python pytorch virtualenv mlflow mlops

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python machine-learning jupyter-notebook data-science mlops

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pytorch amazon-cloudformation amazon-sagemaker endpoint mlops

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conv-neural-network mlflow mlops

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amazon-web-services monitoring serverless mlops

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azure azure-devops azure-machine-learning-service mlops

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terraform terraform-provider-azure

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amazon-web-services flask amazon-ec2 mlops

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python machine-learning scikit-learn pipeline mlops

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python json pytorch mlops algorithmia

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mlops voxel51 fiftyone image-annotations

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r docker podman renv

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python-3.x apache-spark pyspark

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azure azure-devops azure-databricks mlflow

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kubernetes logging airflow mlops kubernetesexecutor

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devops mlops clearml trains

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python tensorflow distributed-computing distributed mlops

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amazon-web-services amazon-iam aws-glue amazon-sagemaker mlops

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mlops google-cloud-vertex-ai

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jupyter-notebook kubeflow kubeflow-pipelines mlops

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python artificial-intelligence kedro mlops

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python azure-machine-learning-service azureml azureml-python-sdk

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azure machine-learning azureml-python-sdk mlops

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docker jenkins jenkins-plugins

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python-3.x mlflow mlops

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azure azure-keyvault azure-managed-identity azureml mlops

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python tensorflow machine-learning keras mlops

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machine-learning aws-lambda serverless amazon-ecr mlops

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python python-3.x sqlite mlops

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azure devops cicd mlops

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google-kubernetes-engine google-ai-platform machine-learning-model

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data-science dashboard amazon-sagemaker amazon-quicksight mlops

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python machine-learning mlflow mlops

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azure azure-devops continuous-integration azure-cli mlops

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deep-learning tfx mscoco

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google-cloud-platform pytorch continuous-integration continuous-deployment mlops

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python django mongodb django-rest-framework djongo

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tensorflow2.0 tensorflow-serving

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clearml trains

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docker kubernetes provisioning kubernetes-pvc kubeflow-pipelines

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azure azure-machine-learning-service

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azure azure-devops azure-pipelines azure-pipelines-release-pipeline azureml

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azure docker

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python deployment mlflow mlops

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amazon-web-services machine-learning aws-lambda amazon-sagemaker mlops

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amazon-sagemaker hyperparameters mlops

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python azure

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tensorflow pytorch tfx mlops

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server jupyter-notebook ipython jupyter-lab mlops

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tensorflow-serving

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grafana kubeflow mlops

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kubernetes google-cloud-platform jupyter-lab kubeflow-pipelines mlops

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python json curl mlflow mlops

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azure azure-devops automl mlops

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machine-learning kubernetes model microservices mlops

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kubernetes mlflow seldon

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google-colaboratory mlflow mlops

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git amazon-web-services github amazon-cloudformation amazon-sagemaker

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git machine-learning continuous-integration dvc mlops

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kubeflow

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apache-spark mlflow

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comet mlflow mlops

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r mlflow

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pandas scikit-learn databricks xgboost mlflow

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amazon-s3 docker-compose mlflow

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python routes mlflow serving

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mlflow

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docker mlflow docker-in-docker

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class persistence databricks

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pyspark databricks onnx mlflow

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docker azure-blob-storage dagster azurite

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tensorflow machine-learning keras deep-learning mlflow

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tensorflow databricks tensorboard mlflow

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tracking artifact mlflow

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google-app-engine http-status-code-405 mlflow

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machine-learning keras mlflow

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python azure mlflow azureml kedro

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pandas dataframe csv

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mlflow aiml

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python machine-learning databricks mlflow

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python machine-learning data-science databricks random-forest

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apache-spark pyspark scikit-learn mlflow mlops

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databricks azure-databricks mlflow

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python conda mlflow

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nginx pam mlflow

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python scikit-learn mlflow mlops

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pandas apache-spark pyspark mlflow

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python apache-spark pyspark apache-spark-sql

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mlflow

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mlflow

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authorization tracking mlflow

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model registry mlflow serve

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python azure mlflow mlops

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azure-databricks mlflow

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python bash ubuntu terminal mlflow

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kubernetes yaml jupyterhub persistent-volumes

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python-3.x pickle torch mlflow

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r rstudio mlflow

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python tensorflow keras mlflow tensorflow-probability

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python mlflow azureml kedro mlops

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python mlflow kedro mlops

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python mlflow spacy-3

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machine-learning deep-learning pytorch mlflow

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apache-spark pyspark databricks fasttext mlflow

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mlflow

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mlflow

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python-3.x conda virtual-environment

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mlflow

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apache-spark pyspark databricks h2o sparkling-water

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rest databricks mlflow

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apache-spark pyspark mlflow databricks-connect

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python dask azureml opencensus azureml-python-sdk

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docker pip mlflow

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python scikit-learn mlflow

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amazon-web-services amazon-sagemaker

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mlflow pmml

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python gridsearchcv mlflow

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python azure tensorflow mlflow

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http port mlflow

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python mlflow

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minio mlflow

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python azure azureml azureml-python-sdk

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amazon-s3 minio mlflow mlops

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python pip mlflow

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python pytorch virtualenv mlflow mlops

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azure tensorflow opencv azure-machine-learning-studio horovod

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pyspark databricks random-forest apache-spark-mllib mlflow

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apache-spark pyspark kerberos mlflow hdp

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python yaml mlflow

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python mysql docker minio mlflow

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conv-neural-network mlflow mlops

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databricks azure-databricks mlflow

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python mlflow

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r mlflow

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mlflow databricks-ml

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python minio mlflow

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xgboost mlflow

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amazon-web-services data-science amazon-ecr mlflow mlops

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python mlflow

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docker mlflow

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amazon-web-services docker amazon-sagemaker amazon-ecr mlflow

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pyspark apache-spark-sql user-defined-functions databricks mlflow

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r machine-learning mlflow

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python docker mlflow

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python list indexing pytorch-lightning

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python tensorflow scikit-learn databricks mlflow

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mlflow

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docker minio

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python api mlflow

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python dataframe tensorflow tensorflow-serving mlflow

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pytest

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azure machine-learning migration databricks mlflow

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pyspark classification metrics mlflow

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bash docker dockerfile

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metrics mlflow

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tensorflow keras mlflow

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python pytorch google-colaboratory pytorch-lightning

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uri tracking mlflow

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python azure databricks azure-databricks mlflow

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azure azure-devops azure-databricks mlflow

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current editor: ?
tags:
    in graph:
       
      total nodes:  extend
      merged nodes:
      unmerge all
      copy to global
      Word Count Unique Lemmas Characters Lemmas Density
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      Show Nodes with Degree > 0:

      0 0

      Filter Graphs:


      Filter Time Range
      from: 0
      to: 0


      Recalculate Metrics   Reset Filters
            
      Hide Labels for Nodes < 0:

      0 0

      Default Label Size: 0

      0 20



      Edges Type:



      Layout Type:


       

      Reset to Default
      network structure:
      ×  ⁝⁝ 
      ×  ⁝⁝ 
      Network Structure Insights
       
      mind-viral immunity:
      N/A
        ?
      stucture:
      N/A
        ?
      The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

      In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
      The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
      The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

      Modularity
      0
      Influence Distribution
      0
      %
      Topics Nodes in Top Topic Components Nodes in Top Comp
      0
      0
      %
      0
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      %
      Nodes Av Degree Density Weighed Betweenness
      0
      0
      0
      0
       

      Narrative Influence Propagation:
        ?
      The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

      The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

      The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
      propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
      We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

      Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

      For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

      Degree Distribution:
        calculate & show   ?
      (based on kolmogorov-smirnov test) ?   switch to linear
      Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

      This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
      If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

      Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
      ×  ⁝⁝ 
         
      Main Topical Groups:

      N/A
      +     full stats   ?     show categories

      The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

      We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
      Most Influential Elements:
      N/A
      +     Reveal Non-obvious   ?

      We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

      Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

      The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
      Network Structure:
      N/A



      Reset Graph   Export: Show Options
      Action Advice:
      N/A
      Structural Gap
      (ask a research question that would link these two topics):
      N/A
      Reveal the Gap   Generate a Question   ?
       
      A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

      This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

      Latent Topical Brokers
      :
      N/A
      ?

      These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

      These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

      Emerging Keywords
      N/A

      Evolution of Topics
      (frequency / time) ?
      The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.

      Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

       
      Main Topics
      (according to Latent Dirichlet Allocation):
      loading...
       ?  

      LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

      We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
      Most Influential Words
      (main topics and words according to LDA):
      loading...

      We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

      Sentiment Analysis

      positive: | negative: | neutral:
      reset filter    ?  

      We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

      The approach is based on AFINN and Emoji Sentiment Ranking

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
      Show Overlapping Nodes Only

      ⤓ Download as CSV  ⤓ Download an Excel File

      Top Relations / Bigrams
      (both directions):

      ⤓ Download   ⤓ Directed Bigrams CSV   ?

      The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

      As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
      Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

       
      We will build two graphs:
      1) Google search results for your query;
      2) Related searches for your query (Google's SERP);
      Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
      Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

       
      We will build two graphs:
      1) the content that already exists when you make this search query (informational supply);
      2) what else people are searching for when they make this query (informational demand);
      You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
      Please, enter your query to visualize the search results as a graph, so you can learn more about this topic:

       
         advanced settings    add data manually
      Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

           advanced settings    add data manually

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