Package: qubitai-dltk
AboutOur philosophy is to create a Deep Technologies platform with ethical AI for enterprises that offers meaningful insights and actions.DLTK Unified Deep Learning platform can be leveraged to build solutions that are Application-Specific and Industry-Specific where AI opportunity found by using DLTK SDKs, APIs and Microservices. With best of the breed AI Services from platform pioneers like H2O, Google's TensorFlow, WEKA and a few trusted open-sources models and libraries, we offer custom AI algorithms with co-innovation support.Getting StartedPre-requisiteOpenDLTK: OpenDLTK is collection of open-source docker images, where processing of images, text or structured tabular data is done using state-of-the-art AI models.Please follow the below link for instructions onOpenDLTK InstallationInstallationInstalling through pippipinstallqubitai-dltkInstalling from SourceClone the repogitclonehttps://github.com/dltk-ai/qubitai-dltk.gitSet working directory to qubitai-dltk foldercdqubitai-dltkInstall requirements from requirements.txt filepipinstall-rrequirements.txtUsageA detailed documentation is presenthere, on how to use various services supported by DLTK,
to verify whether all setup are done properly, we will be using a sample NLP code to analyze sentiment of the input text.Exampleimportdltk_aiclient=dltk_ai.DltkAiClient(base_url='http://localhost:8000')text="The product is very easy to use and has got a really good life expectancy."sentiment_analysis_response=client.sentiment_analysis(text)print(sentiment_analysis_response)Important Parameters:APIkey: a valid API key generated by following steps as shownherebase_url: The base_url is the url for the machine where base service is installed. (default:http://localhost:8000)Expected Output{"nltk_vader":{"emotion":"POSITIVE","scores":{"negative":0.0,"neutral":0.653,"positive":0.347,"compound":0.7496}}}ServicesMachine LearningML Scikit-  This Microservice uses widely used Scikit package for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.ML H2O- This Microservice uses H2O.ai python SDK for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.ML Weka- This Microservice uses WEKA for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.Example NotebooksML Classification Colab NotebookML Regrression Colab NotebookML Clustering Colab NotebookNatural Language Processing (NLP)This microservice provides features like Sentiment analysis, Name Entity Recognition, Tag Extraction using widely usedSpacyandNLTKpackage. It also provide support
for various AI engines like Azure & IBM.Example NotebookNLP Colab NotebookComputer VisionImage Classification- This microservice classify images into various classes using pretrained model and also using supported AI Engines.Object Detection- This microservice detect objects in Images provided by user using pretrained model and using supported AI Engines.Example NotebooksImage Classification Colab NotebookObject Detection Colab NotebookFace Analytics Colab NotebookNoteTo use third party AI engines like Microsoft Azure & IBM watson, please ensure that its credentials were configured while setting up openDLTK.DocumentationFor more detail on DLTK features & usage please referDLTK SDK Client DocumentationLicenseThe content of this project itself is licensed underGNU LGPL, Version 3 (LGPL-3)TeamFounding MemberMentorLead MaintainerCore ContributorFor more details you can reach us at QubitAI Email-ID -connect@qubitai.tech