Introduction to Machine Learning on IBM Watson Studio

Lisa Jung
Upkar Lidder

http://bit.ly/ibm-cloud-summit-2019

IBM Developer

http://bit.ly/upkar-autoai

> ulidder@us.ibm.com
> @lidderupk
> upkar.dev

Prerequisites

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1. Create IBM Cloud Account using THIS URL

3. If you already have an account, use the above URL to sign into your IBM Cloud account.

2. Check your email and activate your account. Once activated, log back into your IBM Cloud account using the link above.

http://bit.ly/ibm-cloud-summit-2019

ML Hype

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ML Map - scikit-learn 

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Unsupervised Learning

Supervised Learning

  • Classification
  • Regression
  • Clustering
  • Dimensionality Reduction

https://scikit-learn.org/stable/tutorial/basic/tutorial.html#machine-learning-the-problem-setting

ML Lifecycle

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Watson Studio & Watson Machine Learning

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Watson Studio

IBM Watson Studio 

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AutoAI

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IBM Watson Studio - project based development platform 

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Workshop - Goals 

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Successfully Create, Store and Deploy a Linear Regression Model on IBM Cloud using Watson Studio and Watson Machine Learning Services.

Question - predict median house price [MEDV] for Boston area 

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http://bit.ly/boston-house-csv

Linear regression - try to fit a line 

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Median House Price

Property Tax

Y = ⍺ + βx

Linear regression - loss function to get best fit 

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Steps

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  1. Sign up / Log into IBM Cloud - http://bit.ly/ibm-ml
  2. Create Watson Studio Service.
  3. Sign into Watson Studio and create a new Data Science Project. It also creates a Cloud Object Store for you.
  4. Upload csv data to your project.
  5. Add a new AutoAI Experiment to your project.
  6. Create a ML Model and save it to IBM Cloud.
  7. Create a new deployment on IBM Cloud.
  8. Test your model !

Step 1 - sign up/ log into IBM Cloud 

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Step 2 - locate Watson Studio in Catalog 

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Step 3 - create Watson Studio instance 

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Step 3 - already have Watson Studio? Find it in Resources 

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Step 4 - launch Watson Studio 

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Step 5 - create a new project 

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Step 6 - pick Data Science starter 

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Step 6a - pick region [US South] 

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Step 7 - give the project a name and assign COS 

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Step 8 - open asset tab, this is your goto page! 

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Step 9 - create a new AutoAI experiment 

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Step 10 - adding training data 

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http://bit.ly/boston-house-csv

Step 11 - pick target column to predict [MEDV] 

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Step 11a - change model and metric if needed 

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Step 12 - run experiment 

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Step 13a - sit back and relax! 

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Step 13b - explore different models in the pipeline 

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Step 14 - save the best model 

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Step 15a - view the model 

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Step 15b - view the model, another way 

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Step 16 - add a new deployment 

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1

2

Step 17 - ensure that deployment is successful with ready status 

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Step 18a - implementation / test the deployed model 

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{"input_data":[{
        "fields": ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT"],
        "values": [[0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98]]
}]}

Step 18b - implementation / test the deployed model 

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AutoAI - Behind the scenes

Data pre-processing

  • analyze, clean and prepare raw data for ML
  • automatically detects and categorizes features based on data type
  • missing value imputation
  • feature encoding
  • feature scaling

Automated model selection

  • test and rank candidate estimators
  • select the best performing estimator with the ranking choice made by the user

Automated Feature Engineering

  • transform raw data into combination of features that best fit the model

Hyperparameter Optimization

  • refine the best performing model
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AutoAI - Supported Estimators

http://bit.ly/autoai-estimators

Watson Machine Learning

WML - Supported Frameworks as of 06.21.19 

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IBM Watson Machine Learning 

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IBM Watson Machine Learning Client 

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http://wml-api-pyclient.mybluemix.net/index.html

WML - create scikit-learn linear regression model 

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from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)

# Create a new Linear Regression Model
LR_model = LinearRegression()

# Train the model
LR_model.fit(X_train, y_train)

# store actual and predited data to draw chart
predicted = LR_model.predict(X_test)
actual = y_test


# The coefficients
print('Coefficients: \n', LR_model.coef_)
# The mean squared error
print("Mean squared error: %.2f"
      % mean_squared_error(actual, predicted))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(actual, predicted))
Output

👉🏽

WML - evaluation metrics 

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AutoAI

Notebook

WML - get Machine Learning service credentials 

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WML - get Machine Learning service credentials 

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WML - save scikit-learn linear regression model 

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# we will use WML to work with IBM Machine Learning Service
from watson_machine_learning_client import WatsonMachineLearningAPIClient

# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard
wml_credentials = {
}

# Instantiate WatsonMachineLearningAPIClient
from watson_machine_learning_client import WatsonMachineLearningAPIClient
client = WatsonMachineLearningAPIClient( wml_credentials )

# store the model
published_model = client.repository.store_model(model=LR_model,
                                                meta_props={'name':'upkar-housing-linear-reg'},
                                                training_data=X_train, training_target=y_train)

WML - deploy scikit-learn linear regression model 

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import json

# grab the model from IBM Cloud
published_model_uid = client.repository.get_model_uid(published_model)

# create a new deployment for the model
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")

#get the scoring endpoint
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)
print(scoring_endpoint)

#use the scoring endpoint to predict house median price some test data
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}
predictions = client.deployments.score(scoring_endpoint, scoring_payload)
print(json.dumps(predictions, indent=2))

WML - deploy scikit-learn linear regression model 

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WML - try it out on your own ! Step1 - Sign into IBM Cloud 

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Step2 - create a new notebook from URL

Grab the FULL URL from : http://bit.ly/boston-house-notebook

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Step3 - paste your WML credentials and run each cell!

Grab the FULL URL from : 

http://bit.ly/boston-house-notebook

IBM Code Patterns

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Thank you

 

Let's chat !

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Upkar Lidder, IBM

 

@lidderupk

https://github.com/lidderupk/

ulidder@us.ibm.com

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