GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cpd3.5/notebooks/python_sdk/deployments/custom_library/Use scikit-learn and custom library to predict temperature.ipynb
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Kernel: Python 3

Use scikit-learn and custom library to predict temperature with `ibm-watson-machine-learning`

This notebook contains steps and code to train a Scikit-Learn model that uses a custom defined transformer and use it with Watson Machine Learning service. Once the model is trained, this notebook contains steps to persist the model and custom defined transformer to Watson Machine Learning Repository, deploy and score it using Watson Machine Learning python client.

In this notebook, we use GNFUV dataset that contains mobile sensor readings data about humidity and temperature from Unmanned Surface Vehicles in a test-bed in Athens, to train a Scikit-Learn model for predicting the temperature.

Some familiarity with Python is helpful. This notebook uses Python-3.7, scikit-learn-0.23.1

Learning goals

The learning goals of this notebook are:

Train a model with custom defined transformer
Persist the custom defined transformer and the model in Watson Machine Learning repository.
Deploy the model using Watson Machine Learning Service
Perform predictions using the deployed model

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Contact with your Cloud Pack for Data administrator and ask him for your account credentials

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform url, your username and password.

In [1]:

username = 'PASTE YOUR USERNAME HERE'
password = 'PASTE YOUR PASSWORD HERE'
url = 'PASTE THE PLATFORM URL HERE'

In [2]:

wml_credentials = {
    "username": username,
    "password": password,
    "url": url,
    "instance_id": 'openshift',
    "version": '3.5'
}

Install and import the `ibm-watson-machine-learning` package

Note: ibm-watson-machine-learning documentation can be found here.

In [ ]:

!pip install -U ibm-watson-machine-learning

In [3]:

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

Click New Deployment Space
Create an empty space
Go to space Settings tab
Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

In [4]:

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

In [ ]:

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set space which you will be using.

In [4]:

client.set.default_space(space_id)

Out[4]:

'SUCCESS'

2. Install the library containing custom transformer

Library - linalgnorm-0.1 is a python distributable package that contains the implementation of a user defined Scikit-Learn transformer - LNormalizer .
Any 3rd party libraries that are required for the custom transformer must be defined as the dependency for the corresponding library that contains implementation of the transformer.

In this section, we will create the library and install it in the current notebook environment.

In [4]:

!mkdir -p linalgnorm-0.1/linalg_norm

Define a custom scikit transformer.

In [5]:

%%writefile linalgnorm-0.1/linalg_norm/sklearn_transformers.py

from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np


class LNormalizer(BaseEstimator, TransformerMixin):
    def __init__(self, norm_ord=2):
        self.norm_ord = norm_ord
        self.row_norm_vals = None

    def fit(self, X, y=None):
        self.row_norm_vals = np.linalg.norm(X, ord=self.norm_ord, axis=0)

    def transform(self, X, y=None):
        return X / self.row_norm_vals

    def fit_transform(self, X, y=None):
        self.fit(X, y)
        return self.transform(X, y)

    def get_norm_vals(self):
        return self.row_norm_vals

Out[5]:

Writing linalgnorm-0.1/linalg_norm/sklearn_transformers.py

Wrap created code into Python source distribution package.

In [6]:

%%writefile linalgnorm-0.1/linalg_norm/__init__.py

__version__ = "0.1"

Out[6]:

Writing linalgnorm-0.1/linalg_norm/__init__.py

In [7]:

%%writefile linalgnorm-0.1/README.md

A simple library containing a simple custom scikit estimator.

Out[7]:

Writing linalgnorm-0.1/README.md

In [8]:

%%writefile linalgnorm-0.1/setup.py

from setuptools import setup

VERSION='0.1'
setup(name='linalgnorm',
      version=VERSION,
      url='https://github.ibm.com/NGP-TWC/repository/',
      author='IBM',
      author_email='[email protected]',
      license='IBM',
      packages=[
            'linalg_norm'
      ],
      zip_safe=False
)

Out[8]:

Writing linalgnorm-0.1/setup.py

In [9]:

%%bash

cd linalgnorm-0.1
python setup.py sdist --formats=zip
cd ..
mv linalgnorm-0.1/dist/linalgnorm-0.1.zip .
rm -rf linalgnorm-0.1

Out[9]:

running sdist
running egg_info
creating linalgnorm.egg-info
writing linalgnorm.egg-info/PKG-INFO
writing dependency_links to linalgnorm.egg-info/dependency_links.txt
writing top-level names to linalgnorm.egg-info/top_level.txt
writing manifest file 'linalgnorm.egg-info/SOURCES.txt'
reading manifest file 'linalgnorm.egg-info/SOURCES.txt'
writing manifest file 'linalgnorm.egg-info/SOURCES.txt'
running check
creating linalgnorm-0.1
creating linalgnorm-0.1/linalg_norm
creating linalgnorm-0.1/linalgnorm.egg-info
copying files to linalgnorm-0.1...
copying README.md -> linalgnorm-0.1
copying setup.py -> linalgnorm-0.1
copying linalg_norm/__init__.py -> linalgnorm-0.1/linalg_norm
copying linalg_norm/sklearn_transformers.py -> linalgnorm-0.1/linalg_norm
copying linalgnorm.egg-info/PKG-INFO -> linalgnorm-0.1/linalgnorm.egg-info
copying linalgnorm.egg-info/SOURCES.txt -> linalgnorm-0.1/linalgnorm.egg-info
copying linalgnorm.egg-info/dependency_links.txt -> linalgnorm-0.1/linalgnorm.egg-info
copying linalgnorm.egg-info/not-zip-safe -> linalgnorm-0.1/linalgnorm.egg-info
copying linalgnorm.egg-info/top_level.txt -> linalgnorm-0.1/linalgnorm.egg-info
Writing linalgnorm-0.1/setup.cfg
creating dist
creating 'dist/linalgnorm-0.1.zip' and adding 'linalgnorm-0.1' to it
adding 'linalgnorm-0.1'
adding 'linalgnorm-0.1/linalg_norm'
adding 'linalgnorm-0.1/linalgnorm.egg-info'
adding 'linalgnorm-0.1/PKG-INFO'
adding 'linalgnorm-0.1/README.md'
adding 'linalgnorm-0.1/setup.py'
adding 'linalgnorm-0.1/setup.cfg'
adding 'linalgnorm-0.1/linalg_norm/sklearn_transformers.py'
adding 'linalgnorm-0.1/linalg_norm/__init__.py'
adding 'linalgnorm-0.1/linalgnorm.egg-info/PKG-INFO'
adding 'linalgnorm-0.1/linalgnorm.egg-info/not-zip-safe'
adding 'linalgnorm-0.1/linalgnorm.egg-info/SOURCES.txt'
adding 'linalgnorm-0.1/linalgnorm.egg-info/top_level.txt'
adding 'linalgnorm-0.1/linalgnorm.egg-info/dependency_links.txt'
removing 'linalgnorm-0.1' (and everything under it)

Install the downloaded library using pip command

In [6]:

!pip install linalgnorm-0.1.zip

Out[6]:

Processing ./linalgnorm-0.1.zip
Building wheels for collected packages: linalgnorm
  Building wheel for linalgnorm (setup.py) ... done
  Created wheel for linalgnorm: filename=linalgnorm-0.1-py3-none-any.whl size=1670 sha256=5416b34c623f8502515a75d8f9de1f6fce41fe55cd31ab9ab87863e6f7f9df23
  Stored in directory: /Users/jansoltysik/Library/Caches/pip/wheels/78/00/7b/c263b6176f7c38c807f442edaa5f11a3e7a2cbcc5fa07b2673
Successfully built linalgnorm
Installing collected packages: linalgnorm
  Attempting uninstall: linalgnorm
    Found existing installation: linalgnorm 0.1
    Uninstalling linalgnorm-0.1:
      Successfully uninstalled linalgnorm-0.1
Successfully installed linalgnorm-0.1

3. Download training dataset and prepare training data

Download the data from UCI repository - https://archive.ics.uci.edu/ml/machine-learning-databases/00452/GNFUV USV Dataset.zip

In [7]:

!rm -rf dataset
!mkdir dataset

In [8]:

!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00452/GNFUV%20USV%20Dataset.zip --output-document=dataset/gnfuv_dataset.zip

Out[8]:

--2020-12-08 12:45:12--  https://archive.ics.uci.edu/ml/machine-learning-databases/00452/GNFUV%20USV%20Dataset.zip
Resolving archive.ics.uci.edu... 128.195.10.252
Connecting to archive.ics.uci.edu|128.195.10.252|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 501978 (490K) [application/x-httpd-php]
Saving to: 'dataset/gnfuv_dataset.zip'

dataset/gnfuv_datas 100%[===================>] 490.21K   119KB/s    in 4.1s    

2020-12-08 12:45:17 (119 KB/s) - 'dataset/gnfuv_dataset.zip' saved [501978/501978]

In [9]:

!unzip dataset/gnfuv_dataset.zip -d dataset

Out[9]:

Archive:  dataset/gnfuv_dataset.zip
  inflating: dataset/pi2/gnfuv-temp-exp1-55d487b85b-5g2xh_1.0.csv  
  inflating: dataset/pi3/gnfuv-temp-exp1-55d487b85b-2bl8b_1.0.csv  
  inflating: dataset/pi4/gnfuv-temp-exp1-55d487b85b-xcl97_1.0.csv  
  inflating: dataset/pi5/gnfuv-temp-exp1-55d487b85b-5ztk8_1.0.csv  
  inflating: dataset/README.pdf      

Create pandas datafame based on the downloaded dataset

In [10]:

import json
import pandas as pd
import numpy as np
import os
from datetime import datetime
from json import JSONDecodeError

In [11]:

home_dir = './dataset'
pi_dirs = os.listdir(home_dir)

data_list = []
base_time = None
columns = None

for pi_dir in pi_dirs:
    if 'pi' not in pi_dir:
        continue
    curr_dir = os.path.join(home_dir, pi_dir)
    data_file = os.path.join(curr_dir, os.listdir(curr_dir)[0])
    with open(data_file, 'r') as f:
        line = f.readline().strip().replace("'", '"')
        while line != '':
            try:
                input_json = json.loads(line)
                sensor_datetime = datetime.fromtimestamp(input_json['time'])
                if base_time is None:
                    base_time = datetime(sensor_datetime.year, sensor_datetime.month, sensor_datetime.day, 0, 0, 0, 0)
                input_json['time'] = (sensor_datetime - base_time).seconds
                data_list.append(list(input_json.values()))
                if columns is None:
                    columns = list(input_json.keys())
            except JSONDecodeError as je:
                pass
            line = f.readline().strip().replace("'", '"')

data_df = pd.DataFrame(data_list, columns=columns)

In [12]:

data_df.head()

Out[12]:

Create training and test datasets from the downloaded GNFUV-USV dataset.

In [13]:

from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

Y = data_df['temperature']
X = data_df.drop('temperature', axis=1)

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=143)

4. Train a model

In this section, you will use the custom transformer as a stage in the Scikit-Learn Pipeline and train a model.

Import the custom transformer

Here, import the custom transformer that has been defined in linalgnorm-0.1.zip and create an instance of it that will inturn be used as stage in sklearn.Pipeline

In [14]:

from linalg_norm.sklearn_transformers import LNormalizer

In [15]:

lnorm_transf = LNormalizer()

Import other objects required to train a model

In [16]:

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression

Now, you can create a Pipeline with user defined transformer as one of the stages and train the model

In [17]:

skl_pipeline = Pipeline(steps=[('normalizer', lnorm_transf), ('regression_estimator', LinearRegression())])
skl_pipeline.fit(X_train.loc[:, ['time', 'humidity']].values, y_train)

Out[17]:

Pipeline(steps=[('normalizer', LNormalizer()),
                ('regression_estimator', LinearRegression())])

In [18]:

y_pred = skl_pipeline.predict(X_test.loc[:, ['time', 'humidity']].values)
rmse = np.mean((np.round(y_pred) - y_test.values)**2)**0.5
print('RMSE: {}'.format(rmse))

Out[18]:

RMSE: 2.213758431322581

5. Persist the model and custom library

In this section, using ibm-watson_machine_learning SDK, you will ...

save the library linalgnorm-0.1.zip in WML Repository by creating a package extension resource
create a Software Specification resource and bind the package resource to it. This Software Specification resource will be used to configure the online deployment runtime environment for a model
bind Software Specification resource to the model and save the model to WML Repository

Create package extension

Define the meta data required to create package extension resource.

The value for file_path in client.package_extensions.LibraryMetaNames.store() contains the library file name that must be uploaded to the WML.

Note: You can also use conda environment configuration file yaml as package extension input. In such case set the TYPE to conda_yml and file_path to yaml file.

client.package_extensions.ConfigurationMetaNames.TYPE = "conda_yml"

In [19]:

meta_prop_pkg_extn = {
    client.package_extensions.ConfigurationMetaNames.NAME: "K_Linag_norm_skl",
    client.package_extensions.ConfigurationMetaNames.DESCRIPTION: "Pkg extension for custom lib",
    client.package_extensions.ConfigurationMetaNames.TYPE: "pip_zip"
}

pkg_extn_details = client.package_extensions.store(meta_props=meta_prop_pkg_extn, file_path="linalgnorm-0.1.zip")
pkg_extn_uid = client.package_extensions.get_uid(pkg_extn_details)
pkg_extn_url = client.package_extensions.get_href(pkg_extn_details)

Out[19]:

Creating package extensions
SUCCESS

Display the details of the package extension resource that was created in the above cell.

In [20]:

details = client.package_extensions.get_details(pkg_extn_uid)

Create software specification and add custom library

Define the meta data required to create software spec resource and bind the package. This software spec resource will be used to configure the online deployment runtime environment for a model.

In [21]:

client.software_specifications.ConfigurationMetaNames.show()

Out[21]:

---------------------------  ----  --------  --------------------------------
META_PROP NAME               TYPE  REQUIRED  SCHEMA
NAME                         str   Y
DESCRIPTION                  str   N
PACKAGE_EXTENSIONS           list  N
SOFTWARE_CONFIGURATION       dict  N         {'platform(required)': 'string'}
BASE_SOFTWARE_SPECIFICATION  dict  Y
---------------------------  ----  --------  --------------------------------

List base software specifications

In [22]:

client.software_specifications.list()

Out[22]:

-----------------------------  ------------------------------------  ----
NAME                           ASSET_ID                              TYPE
default_py3.6                  0062b8c9-8b7d-44a0-a9b9-46c416adcbd9  base
pytorch-onnx_1.3-py3.7-edt     069ea134-3346-5748-b513-49120e15d288  base
scikit-learn_0.20-py3.6        09c5a1d0-9c1e-4473-a344-eb7b665ff687  base
spark-mllib_3.0-scala_2.12     09f4cff0-90a7-5899-b9ed-1ef348aebdee  base
ai-function_0.1-py3.6          0cdb0f1e-5376-4f4d-92dd-da3b69aa9bda  base
shiny-r3.6                     0e6e79df-875e-4f24-8ae9-62dcc2148306  base
pytorch_1.1-py3.6              10ac12d6-6b30-4ccd-8392-3e922c096a92  base
scikit-learn_0.22-py3.6        154010fa-5b3b-4ac1-82af-4d5ee5abbc85  base
default_r3.6                   1b70aec3-ab34-4b87-8aa0-a4a3c8296a36  base
tensorflow_1.15-py3.6          2b73a275-7cbf-420b-a912-eae7f436e0bc  base
pytorch_1.2-py3.6              2c8ef57d-2687-4b7d-acce-01f94976dac1  base
spark-mllib_2.3                2e51f700-bca0-4b0d-88dc-5c6791338875  base
pytorch-onnx_1.1-py3.6-edt     32983cea-3f32-4400-8965-dde874a8d67e  base
spark-mllib_3.0-py37           36507ebe-8770-55ba-ab2a-eafe787600e9  base
spark-mllib_2.4                390d21f8-e58b-4fac-9c55-d7ceda621326  base
xgboost_0.82-py3.6             39e31acd-5f30-41dc-ae44-60233c80306e  base
pytorch-onnx_1.2-py3.6-edt     40589d0e-7019-4e28-8daa-fb03b6f4fe12  base
ai-function_0.2-py3.6          435bfa8f-ddae-549a-826a-894368887231  base
spark-mllib_2.4-r_3.6          49403dff-92e9-4c87-a3d7-a42d0021c095  base
xgboost_0.90-py3.6             4ff8d6c2-1343-4c18-85e1-689c965304d3  base
pytorch-onnx_1.1-py3.6         50f95b2a-bc16-43bb-bc94-b0bed208c60b  base
spark-mllib_2.4-scala_2.11     55a70f99-7320-4be5-9fb9-9edb5a443af5  base
autoai-obm_2.0                 5c2e37fa-80b8-5e77-840f-d912469614ee  base
spss-modeler_18.1              5c3cad7e-507f-4b2a-a9a3-ab53a21dee8b  base
autoai-kb_3.1-py3.7            632d4b22-10aa-5180-88f0-f52dfb6444d7  base
spss-modeler_18.2              687eddc9-028a-4117-b9dd-e57b36f1efa5  base
pytorch-onnx_1.2-py3.6         692a6a4d-2c4d-45ff-a1ed-b167ee55469a  base
do_12.9                        75a3a4b0-6aa0-41b3-a618-48b1f56332a6  base
spark-mllib_2.4-py37           7abc992b-b685-532b-a122-a396a3cdbaab  base
caffe_1.0-py3.6                7bb3dbe2-da6e-4145-918d-b6d84aa93b6b  base
cuda-py3.6                     82c79ece-4d12-40e6-8787-a7b9e0f62770  base
hybrid_0.1                     8c1a58c6-62b5-4dc4-987a-df751c2756b6  base
pytorch-onnx_1.3-py3.7         8d5d8a87-a912-54cf-81ec-3914adaa988d  base
caffe-ibm_1.0-py3.6            8d863266-7927-4d1e-97d7-56a7f4c0a19b  base
spss-modeler_17.1              902d0051-84bd-4af6-ab6b-8f6aa6fdeabb  base
do_12.10                       9100fd72-8159-4eb9-8a0b-a87e12eefa36  base
do_py3.7                       9447fa8b-2051-4d24-9eef-5acb0e3c59f8  base
spark-mllib_3.0-r_3.6          94bb6052-c837-589d-83f1-f4142f219e32  base
cuda-py3.7                     9a44990c-1aa1-4c7d-baf8-c4099011741c  base
hybrid_0.2                     9b3f9040-9cee-4ead-8d7a-780600f542f7  base
autoai-obm_2.0 with Spark 3.0  af10f35f-69fa-5d66-9bf5-acb58434263a  base
tensorflow_2.1-py3.7           c4032338-2a40-500a-beef-b01ab2667e27  base
autoai-kb_3.0-py3.6            d139f196-e04b-5d8b-9140-9a10ca1fa91a  base
spark-mllib_3.0-py36           d82546d5-dd78-5fbb-9131-2ec309bc56ed  base
default_py3.7                  e4429883-c883-42b6-87a8-f419d64088cd  base
-----------------------------  ------------------------------------  ----

Select base software specification to extend

In [23]:

base_sw_spec_uid = client.software_specifications.get_uid_by_name("default_py3.7")

Define new software specification based on base one and custom library

In [24]:

meta_prop_sw_spec = {
    client.software_specifications.ConfigurationMetaNames.NAME: "linalgnorm-0.1",
    client.software_specifications.ConfigurationMetaNames.DESCRIPTION: "Software specification for linalgnorm-0.1",
    client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: {"guid": base_sw_spec_uid}
}

sw_spec_details = client.software_specifications.store(meta_props=meta_prop_sw_spec)
sw_spec_uid = client.software_specifications.get_uid(sw_spec_details)


client.software_specifications.add_package_extension(sw_spec_uid, pkg_extn_uid)

Out[24]:

SUCCESS

'SUCCESS'

Save the model

Define the metadata to save the trained model to WML Repository along with the information about the software spec resource required for the model.

The client.repository.ModelMetaNames.SOFTWARE_SPEC_UID metadata property is used to specify the GUID of the software spec resource that needs to be associated with the model.

In [25]:

model_props = {
    client.repository.ModelMetaNames.NAME: "Temp prediction model with custom lib",
    client.repository.ModelMetaNames.TYPE: 'scikit-learn_0.23',
    client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_uid
    
}

Save the model to the WML Repository and display its saved metadata.

In [26]:

published_model = client.repository.store_model(model=skl_pipeline, meta_props=model_props)

In [27]:

published_model_uid = client.repository.get_model_uid(published_model)
model_details = client.repository.get_details(published_model_uid)
print(json.dumps(model_details, indent=2))

Out[27]:

{
  "entity": {
    "software_spec": {
      "id": "dae27ebc-97b7-450e-bd27-60bd1e5ca198",
      "name": "linalgnorm-0.1"
    },
    "type": "scikit-learn_0.23"
  },
  "metadata": {
    "created_at": "2020-12-08T11:45:55.651Z",
    "id": "a6a27638-71ee-493c-b6b6-d8488958b974",
    "modified_at": "2020-12-08T11:45:57.475Z",
    "name": "Temp prediction model with custom lib",
    "owner": "1000330999",
    "space_id": "83b00166-9047-4159-b777-83dcb498e7ab"
  },
  "system": {
    "warnings": []
  }
}

6 Deploy and Score

In this section, you will deploy the saved model that uses the custom transformer and perform predictions. You will use WML client to perform these tasks.

Deploy the model

In [28]:

metadata = {
    client.deployments.ConfigurationMetaNames.NAME: "Deployment of custom lib model",
    client.deployments.ConfigurationMetaNames.ONLINE: {}
}

created_deployment = client.deployments.create(published_model_uid, meta_props=metadata)

Out[28]:

#######################################################################################

Synchronous deployment creation for uid: 'a6a27638-71ee-493c-b6b6-d8488958b974' started

#######################################################################################

initializing.
ready

------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='0d94523d-a2e7-4d75-b753-648fae333747'
------------------------------------------------------------------------------------------------

Predict using the deployed model

Note: Here we use deployment uid saved in published_model object. In next section, we show how to retrive deployment url from Watson Machine Learning instance.

In [29]:

deployment_uid = client.deployments.get_uid(created_deployment)

Now you can print an online scoring endpoint.

In [30]:

scoring_endpoint = client.deployments.get_scoring_href(created_deployment)
print(scoring_endpoint)

Out[30]:

https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/ml/v4/deployments/0d94523d-a2e7-4d75-b753-648fae333747/predictions

Prepare the payload for prediction. The payload contains the input records for which predictions has to be performed.

In [31]:

scoring_payload = {
    "input_data": [{
        'fields': ["time", "humidity"],
        'values': [[79863, 47]]}]
}

Execute the method to perform online predictions and display the prediction results

In [32]:

predictions = client.deployments.score(deployment_uid, scoring_payload)

In [33]:

print(json.dumps(predictions, indent=2))

Out[33]:

{
  "predictions": [
    {
      "fields": [
        "prediction"
      ],
      "values": [
        [
          14.629242312262988
        ]
      ]
    }
  ]
}

7. Clean up

If you want to clean up all created assets:

experiments
trainings
pipelines
model definitions
models
functions
deployments

please follow up this sample notebook.

8. Summary

You successfully completed this notebook!

You learned how to use a scikit-learn model with custom transformer in Watson Machine Learning service to deploy and score.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

Krishnamurthy Arthanarisamy, is a senior technical lead in IBM Watson Machine Learning team. Krishna works on developing cloud services that caters to different stages of machine learning and deep learning modeling life cycle.

Lukasz Cmielowski, PhD, is a Software Architect and Data Scientist at IBM.

Use scikit-learn and custom library to predict temperature with `ibm-watson-machine-learning`

Learning goals

Contents

1. Set up the environment

Connection to WML

Install and import the `ibm-watson-machine-learning` package

Working with spaces

2. Install the library containing custom transformer

3. Download training dataset and prepare training data

4. Train a model

Import the custom transformer

5. Persist the model and custom library

Create package extension

Create software specification and add custom library

List base software specifications

Select base software specification to extend

Define new software specification based on base one and custom library

Save the model

6 Deploy and Score

Deploy the model

Predict using the deployed model

7. Clean up

8. Summary

Author

Product

Resources

Company

Use scikit-learn and custom library to predict temperature with ibm-watson-machine-learning

Learning goals

Contents

1. Set up the environment

Connection to WML

Install and import the ibm-watson-machine-learning package

Working with spaces

2. Install the library containing custom transformer

3. Download training dataset and prepare training data

4. Train a model

Import the custom transformer

5. Persist the model and custom library

Create package extension

Create software specification and add custom library

List base software specifications

Select base software specification to extend

Define new software specification based on base one and custom library

Save the model

6 Deploy and Score

Deploy the model

Predict using the deployed model

7. Clean up

8. Summary

Author

Use scikit-learn and custom library to predict temperature with `ibm-watson-machine-learning`

Install and import the `ibm-watson-machine-learning` package