new notebooks

Author: hhsecond

DecisionTreeWithApacheSpark.ipynb

@@ -0,0 +1,31 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "349720b2",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ImageClassificationWithPytorch.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c63c7ff8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ImageClassificationWithTensorflow.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0ef479d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scikit_learn/ScikitLearn2Production.ipynb renamed to LinearRegressionWithScikitLearn.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "# Taking ML (Scikit Learn) to highly scalable production using RedisAI\n",
     "Scikit learn is probably the most used machine learning package in the industry. Even though, there are few options readily available for taking deep learning to production (with tfserving etc), there were no widely accepted attempts to build a framework that could help us to take ML to production. Microsoft had build [ONNXRuntime](https://github.com/microsoft/onnxruntime) and the scikit learn exporter for this very purpose. \n",
-    "Very recently RedisAI had announced the support for ONNXRuntime as the third backend (Tensorflow and PyTorch was already supported). This makes us capable of pushing a scikit-learn model through ONNX to a super scalable production. This demo is focusing on showing how this can be accomplished. We'll train a linear regression model for predicting boston house price first. The trained model is then converted to ONNX IR using [sk2onnx](https://github.com/onnx/sklearn-onnx). Third part of the demo shows how to load the onnx binary into RedisAI runtime and how to communicate. "
+    "With the support for ONNXRuntime as the third backend (Tensorflow and PyTorch was already supported) in RedisAI, it is now easy to serve models from almost any traditional ML frameworks. This demo is focusing on showing how this can be accomplished. We'll train a linear regression model for predicting boston house price first. The trained model is then converted to ONNX IR using [sk2onnx](https://github.com/onnx/sklearn-onnx). Third part of the demo shows how to load the onnx binary into RedisAI runtime and how to communicate. "
    ]
   },
   {