Install in that environment ONNX packages: onnxruntime and skl2onnx packages.Create a Python environment (conda or virtual env) that reflects the Python sandbox image.Whitelist a blob container to be accessible by ADX Python sandbox (see the Appendix section of that doc).Enable Python plugin on your ADX cluster (see the Onboarding section of the python() plugin doc).Here we embed few snips just to present the main concepts The complete process can be found in this Jupyter notebook. This model is a binary classifier to predict occupied/empty room based on Temperature, Humidity, Light and CO2 sensors measurements. We build a model to predict room occupancy based on Occupancy Detection data, a public dataset from UCI Repository. Score new data in ADX using the inline python() plugin.Export the ONNX model to a table on ADX or to an Azure blob.Convert the final trained model to ONNX format.Develop your ML model using your favorite framework and tools.To score ONNX models in ADX follow these steps: The Python image is based on Anaconda distribution and contains the most common ML frameworks including Scikit-learn, TensorFlow, Keras and PyTorch.
The Python code is run in multiple sandboxes on ADX existing compute nodes. In this blog we explain how ADX can consume ONNX models, that were built and trained externally, for near real time scoring of new samples that are ingested into ADX.ĪDX supports running Python code embedded in Kusto Query Language (KQL) using the python() plugin. This format enables smooth switching among ML frameworks as well as allowing hardware vendors and others to improve the performance of deep neural networks for multiple frameworks at once by targeting the ONNX representation. ONNX is a system for representation and serialization of ML models to a common file format. To resolve it, Microsoft and Facebook introduced in 2017 ONNX, Open Neural Network Exchange, that was adopted by many companies including AWS, IBM, Intel, Baidu, Mathworks, NVIDIA and many more. On one hand this variety is very good – you can find the most convenient algorithm and framework for your scenario, but on the other hand it creates an interoperability issue, as usually the ML scoring is done on infrastructure which is different from the one used for the training. ( here is a nice overview of ML algorithms, tools and frameworks). These models can be built by various frameworks and/or packages like Scikit-learn, Tensorflow, CNTK, Keras, Caffe2, PyTorch etc. There are many types of models such as Bayesian models, decision trees and forests, regressions, deep neural networks and many more. ADX scoring is done on its compute nodes, in distributed manner near the data, thus achieving the best performance with minimal latency. For ADX users, the best solution for scoring is directly in ADX. Scoring usually needs to be done at scale with minimal latency, processing large sets of new records. This is actually the business goal for building the model. ML Scoring is the process of applying the model on new data to get insights and predictions. Once the model meets the required quality it is serialized and saved for scoring. This process is usually done using data science tools such as Jupyter, P圜harm, VS Code, Matlab etc. They fetch the training data, clean it, engineer features, try different models and tune parameters, repeating this cycle to improve the model’s quality and accuracy. ML Training is done by researchers/data scientists. Most ML models are built and deployed in two steps: Machine Learning is widely used these days for various data driven tasks including detection of security threats, monitoring IoT devices for predictive maintenance, recommendation systems, financial analysis and many other domains.