SF Data Science: Developing Data Products

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Examples, techniques, and lessons learned building data products over the last 4 years at LinkedIn. Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on building data products leveraging LinkedIn's powerful identity and reputation data. The talk describes some techniques and best practices applied to develop products like LinkedIn Skills & Endorsements. This talk was presented at the SF Data Science Meetup on September 19th, 2013

Technology Education

Developing Data Products
SF Data Science Meetup
Pete Skomoroch @peteskomoroch
September 19 2013
©2012 LinkedIn Corporation. All Rights Reserved.

Developing Data Products
Examples, Techniques, & Lessons Learned

Our Mission
Connect the world’s professionals to make them
more productive and successful.
Our Vision
Create economic opportunity for every
professional in the world.
Members First!

LinkedIn is the leading professional network site
Worldwide Workforce
3,300M+
2
Worldwide
Professionals
640M+
2
LinkedIn Members
238M+
1
©2012 LinkedIn Corporation. All Rights Reserved. 4

LinkedIn profiles represent our professional identity
©2012 LinkedIn Corporation. All Rights Reserved. 5
238MMembers 238M Member
Profiles
1 2

We have a lot of data.
©2012 LinkedIn Corporation. All Rights Reserved.

We have a lot of data.
And (like everyone else), we store it in Hadoop.
©2012 LinkedIn Corporation. All Rights Reserved.

We have a lot of data.
And (like everyone else), we store it in Hadoop.
And people build awesome things with that data.
©2012 LinkedIn Corporation. All Rights Reserved.

Collaborative Filtering: LinkedIn Skill Pages
©2012 LinkedIn Corporation. All Rights Reserved.

Classification: giving structure to unstructured data
©2012 LinkedIn Corporation. All Rights Reserved.
Extract

Clustering & Disambiguation
©2012 LinkedIn Corporation. All Rights Reserved.

De-duplication and Normalization
©2012 LinkedIn Corporation. All Rights Reserved.

©2012 LinkedIn Corporation. All Rights Reserved. 15
Network Algorithms: Relevance & Ranking

Prediction: Personalized Skill Recommendations
©2012 LinkedIn Corporation. All Rights Reserved.

Skill Endorsements: Over 2 Billion and Growing
©2012 LinkedIn Corporation. All Rights Reserved.

©2012 LinkedIn Corporation. All Rights Reserved. 20
Social Proof and the Skill Endorsement Graph

The Economic Graph: Skills, Jobs, People, Locations…
©2012 LinkedIn Corporation. All Rights Reserved. 21
Location

Lessons learned developing data
products

Collect the right data at the right time

Large amounts of data can reveal new patterns
©2012 LinkedIn Corporation. All Rights Reserved. 24
ProbabilityofJobTitle
Time since graduation

Be wary of “black-box” approaches
©2012 LinkedIn Corporation. All Rights Reserved. 25

Look at your data
©2012 LinkedIn Corporation. All Rights Reserved. 26

Aggregate statistics can be misleading
©2012 LinkedIn Corporation. All Rights Reserved. 27
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1 2 3 4 5 6 7 8 9 10

Build a viewer app, “micro-listen”
©2012 LinkedIn Corporation. All Rights Reserved. 28

Algorithmic intuition: include data geeks in design
©2012 LinkedIn Corporation. All Rights Reserved. 29

OODA: Think like a jet fighter
©2012 LinkedIn Corporation. All Rights Reserved. 30

OODA: Observe, Orient, Decide, Act
©2012 LinkedIn Corporation. All Rights Reserved. 31

OODA: The speed you can move determines victory
©2012 LinkedIn Corporation. All Rights Reserved. 32

Red teaming: what can go wrong likely will
©2012 LinkedIn Corporation. All Rights Reserved. 33

Error data is valuable, analyze it and adapt
©2012 LinkedIn Corporation. All Rights Reserved. 34

Conclusion: tips for developing data products
 Collect the right data at the right time
 Large amounts of data can reveal new patterns
 Be wary of “black box” approaches
 Look at your raw data
 Aggregate statistics can be misleading
 Build and use viewer apps
 Include data geeks in design process
 OODA: Think like a jet fighter
 Red-teaming: anticipate edge cases
 Find opportunity in your error data
©2012 LinkedIn Corporation. All Rights Reserved.

Questions?
@peteskomoroch
©2012 LinkedIn Corporation. All Rights Reserved. 36

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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place. Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects. Here’s what you’ll gain: - Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows. - Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy. - Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency. - Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity. We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic. Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.

Essentials of Automations: Optimizing FME Workflows with Parameters

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SF Data Science: Developing Data Products

2. Developing Data Products Examples, Techniques, & Lessons Learned

3. Our Mission Connect the world’s professionals to make them more productive and successful. Our Vision Create economic opportunity for every professional in the world. Members First!

9. What do we mean by data products?

10. Building products from data at LinkedIn A few examples:  People You May Know  Skills and Endorsements  Year in Review  Network Updates Digest  InMaps  Who’s viewed my profile  Collaborative Filtering  Groups You May Like  and more… ©2012 LinkedIn Corporation. All Rights Reserved.

17.

18.

22. Lessons learned developing data products

23. Collect the right data at the right time

35. Conclusion: tips for developing data products  Collect the right data at the right time  Large amounts of data can reveal new patterns  Be wary of “black box” approaches  Look at your raw data  Aggregate statistics can be misleading  Build and use viewer apps  Include data geeks in design process  OODA: Think like a jet fighter  Red-teaming: anticipate edge cases  Find opportunity in your error data ©2012 LinkedIn Corporation. All Rights Reserved.

Editor's Notes

Mission: For us, fundamentally changing the way the world works begins with our mission statement: To connect the world’s professionals to make them more productive and successful. This means not only helping people to find their dream jobs, but also enabling them to be great at the jobs they’re already in. Vision: But, we’re just getting started. By our measure,there are more than 640 million professionals in the world. And roughly 3.3 billion people in the global workforce. Ultimately, our vision is to create economic opportunity for every professional, which we believe is an especially crucial objective in light of current macroeconomic trends.Our most important core value is that members come first.

SF Data Science: Developing Data Products

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Editor's Notes