cloud/notebooks/python_sdk/deployments/scikit-learn/Use scikit-learn to recognize hand-written digits.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Use scikit-learn to recognize hand-written digits with `ibm-watsonx-ai`\n",
        "\n",
        "This notebook contains steps and code to demonstrate how to persist and deploy locally trained scikit-learn model in watsonx.ai Runtime Service. This notebook contains steps and code to work with [ibm-watsonx-ai](https://pypi.python.org/pypi/ibm-watsonx-ai) library available in PyPI repository. This notebook introduces commands for getting model and training data, persisting model, deploying model, scoring it, updating the model and redeploying it.\n",
        "\n",
        "Some familiarity with Python is helpful. This notebook uses Python 3.11."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Learning goals\n",
        "\n",
        "The learning goals of this notebook are:\n",
        "\n",
        "-  Train sklearn model.\n",
        "-  Persist trained model in watsonx.ai Runtime repository.\n",
        "-  Deploy model for online scoring using client library.\n",
        "-  Score sample records using client library.\n",
        "\n",
        "\n",
        "## Contents\n",
        "\n",
        "This notebook contains the following parts:\n",
        "\n",
        "1.\t[Setup](#setup)\n",
        "2.\t[Explore data and create scikit-learn model](#train)\n",
        "3.\t[Persist externally created scikit model](#upload)\n",
        "4.\t[Deploy and score in a Cloud](#deploy)\n",
        "5.  [Clean up](#cleanup)\n",
        "6.\t[Summary and next steps](#summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"setup\"></a>\n",
        "## 1. Set up the environment\n",
        "\n",
        "Before you use the sample code in this notebook, you must perform the following setup tasks:\n",
        "\n",
        "-  Create a <a href=\"https://cloud.ibm.com/catalog/services/watsonxai-runtime\" target=\"_blank\" rel=\"noopener no referrer\">watsonx.ai Runtime Service</a> instance (a free plan is offered and information about how to create the instance can be found <a href=\"https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/wml-plans.html?context=wx&audience=wdp\" target=\"_blank\" rel=\"noopener no referrer\">here</a>)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Install and import the `ibm-watsonx-ai` and dependecies\n",
        "**Note:** `ibm-watsonx-ai` documentation can be found <a href=\"https://ibm.github.io/watsonx-ai-python-sdk/index.html\" target=\"_blank\" rel=\"noopener no referrer\">here</a>."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!pip install -U ibm-watsonx-ai | tail -n 1\n",
        "!pip install \"scikit-learn==1.3.2\" | tail -n 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Connection to watsonx.ai Runtime\n",
        "\n",
        "Authenticate the watsonx.ai Runtime service on IBM Cloud. You need to provide platform `api_key` and instance `location`.\n",
        "\n",
        "You can use [IBM Cloud CLI](https://cloud.ibm.com/docs/cli/index.html) to retrieve platform API Key and instance location.\n",
        "\n",
        "API Key can be generated in the following way:\n",
        "```\n",
        "ibmcloud login\n",
        "ibmcloud iam api-key-create API_KEY_NAME\n",
        "```\n",
        "\n",
        "In result, get the value of `api_key` from the output.\n",
        "\n",
        "\n",
        "Location of your watsonx.ai Runtime instance can be retrieved in the following way:\n",
        "```\n",
        "ibmcloud login --apikey API_KEY -a https://cloud.ibm.com\n",
        "ibmcloud resource service-instance INSTANCE_NAME\n",
        "```\n",
        "\n",
        "In result, get the value of `location` from the output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Tip**: Your `Cloud API key` can be generated by going to the [**Users** section of the Cloud console](https://cloud.ibm.com/iam#/users). From that page, click your name, scroll down to the **API Keys** section, and click **Create an IBM Cloud API key**. Give your key a name and click **Create**, then copy the created key and paste it below. You can also get a service specific url by going to the [**Endpoint URLs** section of the watsonx.ai Runtime docs](https://cloud.ibm.com/apidocs/machine-learning).  You can check your instance location in your  <a href=\"https://cloud.ibm.com/catalog/services/watsonxai-runtime\" target=\"_blank\" rel=\"noopener no referrer\">watsonx.ai Runtime Service</a> instance details.\n",
        "\n",
        "You can also get service specific apikey by going to the [**Service IDs** section of the Cloud Console](https://cloud.ibm.com/iam/serviceids).  From that page, click **Create**, then copy the created key and paste it below.\n",
        "\n",
        "**Action**: Enter your `api_key` and `location` in the following cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "api_key = 'PASTE YOUR PLATFORM API KEY HERE'\n",
        "location = 'PASTE YOUR INSTANCE LOCATION HERE'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from ibm_watsonx_ai import Credentials\n",
        "\n",
        "credentials = Credentials(\n",
        "    api_key=api_key,\n",
        "    url='https://' + location + '.ml.cloud.ibm.com'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "from ibm_watsonx_ai import APIClient\n",
        "\n",
        "client = APIClient(credentials)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Working with spaces\n",
        "\n",
        "First, you need to create a space that will be used for your work. If you do not have space already created, you can use [Deployment Spaces Dashboard](https://dataplatform.cloud.ibm.com/ml-runtime/spaces?context=cpdaas) to create one.\n",
        "\n",
        "- Click New Deployment Space\n",
        "- Create an empty space\n",
        "- Select Cloud Object Storage\n",
        "- Select watsonx.ai Runtime instance and press Create\n",
        "- Copy `space_id` and paste it below\n",
        "\n",
        "**Tip**: You can also use SDK to prepare the space for your work. More information can be found [here](https://github.com/IBM/watson-machine-learning-samples/blob/master/cloud/notebooks/python_sdk/instance-management/Space%20management.ipynb).\n",
        "\n",
        "**Action**: Assign space ID below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "space_id = 'PASTE YOUR SPACE ID HERE'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can use `list` method to print all existing spaces."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.spaces.list(limit=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To be able to interact with all resources available in watsonx.ai Runtime, you need to set **space** which you will be using."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'SUCCESS'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.set.default_space(space_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"train\"></a>\n",
        "## 2. Explore data and create scikit-learn model\n",
        "In this section, you will prepare and train handwritten digits model using scikit-learn library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.1 Explore data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As a first step, you will load the data from scikit-learn sample datasets and perform a basic exploration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from sklearn import datasets\n",
        "\n",
        "digits = datasets.load_digits()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Loaded toy dataset consists of 8x8 pixels images of hand-written digits.\n",
        "\n",
        "Let's display first digit data and label using **data** and **target**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
            " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
            " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
            " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
            " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
            " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
            " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
            " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n"
          ]
        }
      ],
      "source": [
        "print(digits.data[0].reshape((8, 8)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "digits.target[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In next step, you will count data examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of samples: 1797\n"
          ]
        }
      ],
      "source": [
        "samples_count = len(digits.images)\n",
        "print(\"Number of samples: \" + str(samples_count))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.2. Create a scikit-learn model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Prepare data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this step, you'll split your data into three datasets:\n",
        "- train\n",
        "- test\n",
        "- score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training records: 1257\n",
            "Number of testing records : 360\n",
            "Number of scoring records : 180\n"
          ]
        }
      ],
      "source": [
        "train_data = digits.data[: int(0.7*samples_count)]\n",
        "train_labels = digits.target[: int(0.7*samples_count)]\n",
        "\n",
        "test_data = digits.data[int(0.7*samples_count): int(0.9*samples_count)]\n",
        "test_labels = digits.target[int(0.7*samples_count): int(0.9*samples_count)]\n",
        "\n",
        "score_data = digits.data[int(0.9*samples_count): ]\n",
        "\n",
        "print(\"Number of training records: \" + str(len(train_data)))\n",
        "print(\"Number of testing records : \" + str(len(test_data)))\n",
        "print(\"Number of scoring records : \" + str(len(score_data)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Create pipeline**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, you'll create scikit-learn pipeline."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In ths step, you will import scikit-learn machine learning packages that will be needed in next cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn import preprocessing\n",
        "from sklearn import svm, metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Standardize features by removing the mean and scaling to unit variance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "scaler = preprocessing.StandardScaler()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, define estimators you want to use for classification. Support Vector Machines (SVM) with radial basis function as kernel is used in the following example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "clf = svm.SVC(kernel='rbf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's build the pipeline now. This pipeline consists of transformer and an estimator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline = Pipeline([('scaler', scaler), ('svc', clf)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Train model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, you can train your SVM model by using the previously defined **pipeline** and **train data**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = pipeline.fit(train_data, train_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Evaluate model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can check your **model quality** now. To evaluate the model, use **test data**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation report: \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.99        37\n",
            "           1       0.97      0.97      0.97        34\n",
            "           2       1.00      0.97      0.99        36\n",
            "           3       1.00      0.94      0.97        35\n",
            "           4       0.78      0.97      0.87        37\n",
            "           5       0.97      0.97      0.97        38\n",
            "           6       0.97      0.86      0.91        36\n",
            "           7       0.92      0.97      0.94        35\n",
            "           8       0.91      0.89      0.90        35\n",
            "           9       0.97      0.92      0.94        37\n",
            "\n",
            "    accuracy                           0.94       360\n",
            "   macro avg       0.95      0.94      0.95       360\n",
            "weighted avg       0.95      0.94      0.95       360\n",
            "\n"
          ]
        }
      ],
      "source": [
        "predicted = model.predict(test_data)\n",
        "\n",
        "print(\"Evaluation report: \\n\\n%s\" % metrics.classification_report(test_labels, predicted))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can tune your model now to achieve better accuracy. For simplicity of this example tuning section is omitted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"upload\"></a>\n",
        "## 3. Persist locally created scikit-learn model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this section, you will learn how to store your model in watsonx.ai Runtime repository by using the IBM watsonx.ai SDK."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3.1: Publish model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Publish model in watsonx.ai Runtime repository on Cloud."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define model name, autor name and email."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "sofware_spec_id = client.software_specifications.get_id_by_name(\"runtime-24.1-py3.11\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "metadata = {\n",
        "            client.repository.ModelMetaNames.NAME: 'Scikit model',\n",
        "            client.repository.ModelMetaNames.TYPE: 'scikit-learn_1.3',\n",
        "            client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: sofware_spec_id\n",
        "}\n",
        "\n",
        "published_model = client.repository.store_model(\n",
        "    model=model,\n",
        "    meta_props=metadata,\n",
        "    training_data=train_data,\n",
        "    training_target=train_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3.2: Get model details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "published_model_id = client.repository.get_model_id(published_model)\n",
        "model_details = client.repository.get_details(published_model_id)\n",
        "print(json.dumps(model_details, indent=2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3.3 Get all models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "models_details = client.repository.list_models()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"deploy\"></a>\n",
        "## 4. Deploy and score in a Cloud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this section you will learn how to create online scoring and to score a new data record by using the IBM watsonx.ai SDK."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.1: Create model deployment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create online deployment for published model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "Synchronous deployment creation for uid: '3ea048dc-94de-4716-83de-45cbb84d0a9c' started\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "\n",
            "initializing\n",
            "Note: online_url and serving_urls are deprecated and will be removed in a future release. Use inference instead.\n",
            "\n",
            "ready\n",
            "\n",
            "\n",
            "------------------------------------------------------------------------------------------------\n",
            "Successfully finished deployment creation, deployment_uid='b7b270ac-769a-4263-b7c0-1f32bbf0d141'\n",
            "------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "metadata = {\n",
        "    client.deployments.ConfigurationMetaNames.NAME: \"Deployment of scikit model\",\n",
        "    client.deployments.ConfigurationMetaNames.ONLINE: {}\n",
        "}\n",
        "\n",
        "created_deployment = client.deployments.create(published_model_id, meta_props=metadata)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note**: Here we use deployment url saved in published_model object. In next section, we show how to retrive deployment url from watsonx.ai Runtime instance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "deployment_id = client.deployments.get_id(created_deployment)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now you can print an online scoring endpoint. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "scoring_endpoint = client.deployments.get_scoring_href(created_deployment)\n",
        "print(scoring_endpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can also list existing deployments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.deployments.list()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.2: Get deployment details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "client.deployments.get_details(deployment_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"score\"></a>\n",
        "### 4.3: Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can use the following method to do test scoring request against deployed model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Action**: Prepare scoring payload with records to score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "score_0 = list(score_data[0])\n",
        "score_1 = list(score_data[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "scoring_payload = {\"input_data\": [{\"values\": [score_0, score_1]}]}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Use ``client.deployments.score()`` method to run scoring."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "predictions = client.deployments.score(deployment_id, scoring_payload)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"predictions\": [\n",
            "    {\n",
            "      \"fields\": [\n",
            "        \"prediction\"\n",
            "      ],\n",
            "      \"values\": [\n",
            "        [\n",
            "          5\n",
            "        ],\n",
            "        [\n",
            "          4\n",
            "        ]\n",
            "      ]\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "print(json.dumps(predictions, indent=2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"cleanup\"></a>\n",
        "## 5. Clean up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you want to clean up all created assets:\n",
        "- experiments\n",
        "- trainings\n",
        "- pipelines\n",
        "- model definitions\n",
        "- models\n",
        "- functions\n",
        "- deployments\n",
        "\n",
        "please follow up this sample [notebook](https://github.com/IBM/watson-machine-learning-samples/blob/master/cloud/notebooks/python_sdk/instance-management/Machine%20Learning%20artifacts%20management.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id=\"summary\"></a>\n",
        "## 6. Summary and next steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        " You successfully completed this notebook! You learned how to use scikit-learn machine learning as well as watsonx.ai Runtime for model creation and deployment. Check out our _[Online Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html?context=wx)_ for more samples, tutorials, documentation, how-tos, and blog posts. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Authors\n",
        "\n",
        "**Daniel Ryszka**, Software Engineer\n",
        "\n",
        "**Mateusz Szewczyk**, Software Engineer at watsonx.ai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License."
      ]
    }
  ],
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