Introduction to Machine Learning on IBM Cloud

Upkar Lidder

IBM Developer

http://bit.ly/svldeveloperday

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

ML Hype

@lidderupk

IBM Developer

ML on IBM Cloud - Cognitive Services

@lidderupk

IBM Developer

Natural Language Processing

Visual Recognition

ML on IBM Cloud - Watson Assistant

@lidderupk

IBM Developer

ML on IBM Cloud - Watson Assistant

@lidderupk

IBM Developer

ML on IBM Cloud - NLU [Greta Thunberg]

@lidderupk

IBM Developer

https://natural-language-understanding-demo.ng.bluemix.net

https://gist.github.com/lidderupk/4e1fdc88ee064561f9e7be799e973c16

ML on IBM Cloud - Sentiment Analysis

@lidderupk

IBM Developer

https://tone-analyzer-demo.ng.bluemix.net/

ML on IBM Cloud - Discovery

@lidderupk

IBM Developer

ML on IBM Cloud - Discovery

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IBM Developer

ML on IBM Cloud - Visual Recognition

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IBM Developer

Watson Studio

ML Lifecycle

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IBM Developer

ML Map

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IBM Developer

Unsupervised Learning

Supervised Learning

Classification
Regression

Clustering
Dimensionality Reduction

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

Watson Studio & Watson Machine Learning

@lidderupk

IBM Developer

IBM Watson Studio

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IBM Developer

IBM Watson Studio - project based development platform

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IBM Developer

ML on IBM Cloud - Guided ML

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IBM Developer

To help simplify an AI lifecycle management, AutoAI automates:

Data preparation
Model development
Feature engineering
Hyper parameter optimization

AutoAI

@lidderupk

IBM Developer

@lidderupk

IBM Developer

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

@lidderupk

IBM Developer

AutoAI - Supported Estimators

http://bit.ly/autoai-estimators

@lidderupk

IBM Developer

AutoAI - Example

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Data Profiling

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Data Visualization

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Data Transformation

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Modeling [complete dataset]

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Modeling [small dataset]

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

@lidderupk

IBM Developer

AutoAI - Modeling [experiments]

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

I want to build my own!

😤

😤

Watson Machine Learning

WML - Supported Frameworks as of 06.21.19

@lidderupk

IBM Developer

IBM Watson Machine Learning

@lidderupk

IBM Developer

IBM Watson Machine Learning Client

@lidderupk

IBM Developer

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

WML - create scikit-learn linear regression model

@lidderupk

IBM Developer

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

@lidderupk

IBM Developer

AutoAI

Notebook

WML - get Machine Learning service credentials

@lidderupk

IBM Developer

WML - get Machine Learning service credentials

@lidderupk

IBM Developer

WML - save scikit-learn linear regression model

@lidderupk

IBM Developer

# 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

@lidderupk

IBM Developer

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

@lidderupk

IBM Developer

IBM Code Patterns

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IBM Developer

Model Asset Exchange

@lidderupk

IBM Developer

https://developer.ibm.com/exchanges/models/

Data Asset Exchange

@lidderupk

IBM Developer

https://developer.ibm.com/exchanges/data/

Thank you

Let's chat !

@lidderupk

IBM Developer

Upkar Lidder, IBM

@lidderupk

https://github.com/lidderupk/

ulidder@us.ibm.com

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