After installing MLHub (Section 2.1) we are ready to install MLHub packages. The simplest option is to install curated packages. Such packages are reviewed by the MLHub team and the specific details required to install a package are obtained from a MLHub maintained index. The MLHub team review these packages to ensure their integrity and functionality. There is though no limit to what models can be packaged for MLHub. MLHub is able to be pointed to any git repository and install a package based on the MLHUB.yaml file found there.
The available command lists the available curated packages. These pacakges can be installed simply through the name of the package (the left hand column).
$ ml available The repository 'https://mlhub.ai/' provides the following models: animate 2.1.5 Tell a data narative through animations audit 4.1.0 Classic financial audit predictive classification model. azanomaly 3.1.4 Azure Anomaly Detection. azcv 2.7.2 Azure Computer Vision. azface 2.2.1 Azure Face API demo. azlang 0.0.4 Azure language cognitive service on the cloud. azspeech 4.4.1 Azure Speech cognitive services on the cloud. aztext 2.5.2 Azure Text Analytics cognitive services on the cloud. aztranslate 2.5.3 Azure Text Translation cognitive services on the cloud. barchart 2.0.2 Demonstrate the concept of barcharts. beeswarm 2.0.1 Demonstrate the concept of bee swarm charts. bing 0.1.5 Bing Maps cars 1.0.0 Identify car make and model from a photo. colorize 1.5.9 Demonstrate the concept of photo colorization. cvbp 2.2.0 Computer vision best practices. deepspeech 0.0.3 Deepspeech easyocr 0.0.8 Extract text from images. facedetect 0.2.5 Simple face detection. facematch 0.4.2 Simple face recognition. google 0.0.1 Google Maps iris 2.1.3 Classic iris plant species classifier. kmeans 1.0.0 Kmeans clustering with animation. movies 2.0.4 Movie recommendation using the SAR algorthm. objects 1.6.27 Recognise objects in an image using resnet152. ocsvm 0.0.5 Introducing one-class support vector machine. opencv 1.0.3 OpenCV Computer Vision. patientpaths 0.0.8 Report patient paths for specific scenarios. ports 2.0.2 Demostrate the concept of visualising data. pyiris 0.0.8 Classification models in Python using the iris dataset. pyspeech 0.1.3 Convert audio speech to text across multiple services. rain 5.1.4 Predict if it will rain tomorrow (decision tree and rand... rbm 1.0.6 Recommendations using restricted Boltzmann machine. sar 1.1.6 Smart adaptive recommendations. scatter 2.0.1 Demonstrate the concept of scatter plots. sgnc 0.1.1 Node classification for graphs using StellarGraph. tapwater 0.0.3 Factor analysis for understanding customers webcam 1.1.0 Capture video, process, feed dummy device for Zoom. zynlp 0.0.11 Tweets sentiment analysis. To install a named model, local model file or URL: $ ml install <model>
These are only the curated packages. Any MLHub package can be installed through reference to it’s GitHub repository. See Section 2.5 for details.
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