Embed Size (px)
Citation preview
How Machine Learning Can Transform The Customer ExperienceEvren Korpeoglu, Data Science
Aarthi Srinivasan, Product Management
/Productschool @ProdSchool /ProductmanagementSV
Aarthi Srinivasan- Walmart Labs- 12+ years of combined experience in product
management, consulting and engineering- MBA & MS in Computer Science
www.productschool.com
INTRODUCTIONSEvren Korpeoglu- Walmart Labs Data Scientist- Machine Learning, Big Data, Statistical Modeling
& Optimization for powering real-time Experiences
- Ph.D. Operations Research
3
What to Expect Today?• What is machine learning ?
• Why is it important ?
• How do we use it ?
• Technical Concepts• Examples
What is Machine Learning?
4
1. Science of getting computers to learn or recognize something without being explicitly programmed – Andrew Ng
• Branch of Artificial Intelligence which is a branch of Computer Science• Give lots of data to the computer so that it can figure it out• One of the first examples is the computer checkers program by Arthur Samuel
* - ref: Andew Ng Courses, Big data: A revolution
2. Distinguish big data & machine learning: Big data is the data seed for creating machine learning forests• Big data collects information based on our digital exhaust (crumbs we leave in
the digital world) , demographics, preferences, health etc. • Machine learning will mine this data and model behaviors with interactive
responses based based on this data
5
Why do we need this?1. Tons of applications impacting human health, utility and simplification
Health & Wellness Utilitarian Futuristic
• DNA sampling & diagnosis
• Health reminders & prevention through AI tools
• Correlation studies • Personalized
medicine tablets, diets
• Real time optimized path maps
• Search Ranking• Spam filter on email• News aggregators• Shopping
Recommendation
• Facebook face recognition
• Age recognition (How-old.net)
• Voice recognition – Siri, Alexa
• Driverless cars • Home decoration
6
Key Terms• A set of data used to predict relationships. Data and answers for each
sample. • E.g. A diamond’s size, cut, color and clarity helps predicts the price.
Training Set
• Uses training set to make a prediction.• E.g. Model predicts diamond prices based on past prices.Supervised Learning
• Provide data without suggesting anything so computer can identify patterns or groupings.
• E.g. Customer segmentation, DNA groupings.Unsupervised Learning
• Each distinct measurable data value you select in the training data set.• E.g. A diamonds’ size is one of the feature’s for predicting price.
Features/ Variables / Attributes
• Using the features provided in the training set make a prediction. Fit a curve using the data provided.
• E.g. Price of diamond = X*Cut + Y*Clarity + Z*Size + other features… Supervised: Regression
• A defined set of categories that can be labeled for placing new observations. • E.g. Presence of absence of cancer; Types of diabetesSupervised: Classification
• Process of assigning observations into subsets.• E.g. Customer segment creationsUnsupervised: Clustering
Learning Steps
7
Collect / Update User
Data
1
Create / Update
Training Set data
2
Create / Update
algorithm for training data
Update Algorithm
Validate Algorithm
3Create
predictive model
4
New real-time observations
A/B Test & Launch on production
5
8
Data Wrangling and Feature Extraction
Spam Email Detection
TitleSender Domain
# of Recipients
Email content
Country of Origin
Non-dictionary
Words
Hyperlinks
Address Book
Length of email
• Structured Data (Best)– RDBMS, columnar data– Strict Schema– SQL
• Semi-Structured Data (Better)– JSON, XML– Enforce minimum schema– JSON, XML Parser
• Unstructured Data– Text, Image, Raw email– No Schema– Batch processing– Regular expressions– Map Reduce
GARBAGE IN GARBAGE OUT
9
Model Training
Feature Extraction(Feature vector)
NewText documents
User ActivityImages
Transaction history
Feature Extraction(Feature vector)
Labels
Machine Learning
Algorithm
Training / TestingText documents
User ActivityImages
Transaction history
Predictive Model
Expected Label
Model Evaluation
10
Supervised learning techniques• Linear classifier (numerical functions)• Parametric (Probabilistic functions)
– Naïve Bayes, Hidden Markov models (HMM), Probabilistic graphical models
• Non-parametric (Instance-based functions) – K-nearest neighbors
• Non-metric (Symbolic functions) – Classification and regression tree (CART)
• Aggregation– Bagging (bootstrap + aggregation), Adaboost, Random
forest, Ensemble models
11
Linear Classifiers• Logistic regression
– )
– w with minimum loss– Solve iteratively using gradient descent
• Support vector machine (SVM)– Maximum margin classifier
• Artificial Neural Networks– Inspired from how neurons work– Activation function (Sigmoid, ReLU etc.)– Deep Learning
12
KNN / CART• K-Nearest Neighbors
– Find K nearest training examples– Majority vote– Easy to implement– Not scalable for real time predictions
• Classification and Regression Trees– Easy to interpret for small trees
• Random Forests– Ensemble of decision trees– Usually performs very good
13
Unsupervised Learning• Clustering
– K-means clustering– Spectral clustering
• Dimensionality reduction – Principal component analysis (PCA) – Factor analysis
• Product Recommendations– Collaborative Filtering
• Association Rules– Market Basket Analysis
14
Model Evaluation• Measure model performance
• Optimize model to improve prediction quality
– Feature selection– Hyperparameter tuning
• A/B Testing• Explore/Exploit
• http://en.wikipedia.org/wiki/Precision_and_recall
15
Sample Architecture
-HADOOP- SPARK
PREDICTION ENGINE
REAL TIME DATA
SQL / NO SQLData Base
CLIENT MACHINE LEARNINGSYSTEM
16
Health & Wellness Sen.se Mother (iOT)
17
Amazon Echo & Personalization
18
Houzz Visual Match Deep Learning
19
Wal-Mart Testing Example
20
Sample E-Commerce Applications1. Segment customers (E.g. Millennial college grads, Moms, New Dads, etc.)
2. Personalize experiences for segments (Moms will see unique customer layouts and promotional items compared to dads or teens who love video games)
3. Personalize marketing e-mail and even timing of e-mail delivery
4. Trigger experiences based on customer information or local events (e.g. shipping preferences, Events like birthdays or concerts)
5. Create a personalized basket based on previous purchases or life stage
6. Use BOTs to provide relevant information to users
7. Augmented reality - Provide personalized information for sale items
22
Appendix
Sample Personalization Highlights
Testing Results with test (2MM – 7MM pop)POV Personalized vs. No personalization Personalized POV increased conversion by x%.
POV White listed personalization vs. Full automation Desktop conversion increased by x%.
Layered POV vs. Static POV Conversion increased by x%, PVR increased by x%, Bounce reduced by x%.
Personalized carousels on the home page Increased conversion on Desktop users by x%
Personalized DTC vs. Curated Mother’s day carousel CTR increased by x%.
Upcoming Courses
Silicon ValleyOctober Cohort
Weeknights: October 18th
Weekends: October 15th
Apply Atwww.productschool.com
www.productschool.com
www.productschool.com
Upcoming Workshops
Rsvp On Eventbrite
Sept 28: Product Management Course - Info Session
Oct 5: From Building Products To Managing Them
Oct 12: Risk Management while creating great products: Why no one really cares and what happens because of that
Oct 19: Product Management Happy Hour
