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Examples, techniques, and lessons learned building data products over the last 3 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 was the inaugural UberData Tech Talk, held in SF at Uber HQ.

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Legal risks, rights and obligations are among the most important emerging issues in enterprise data management today, and yet are not well understood, especially considering most CDOs come to the role with a business or IT background, rather than legal training. Bill Tanenbaum is one of the leading technology and intellectual property attorneys in the country, and is joining us on this edition of the CDO Agenda to provide a legal perspective on the issues that organizations should be dealing with today, from the standpoint of data management policy. Among the topics he will address are: Security and Data Breaches Data Vulnerability in Contracts The CDO’s Role in ITS Contracts Privacy is Contextual Data Ownership vs License rights Data as Intellectual Property Data Sharing Is Data an Asset? How Long Should you Keep Data?

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To design effective user-focused services, we need to use data. We need to understand how people are using the service, what works for them and what doesn’t. There can be no service without data. But as designers, we have to focus on user needs. That means we need to address users’ data needs as well as their service needs. We must design good services based on good data that don’t infringe on people’s privacy. This means we have to look at questions like: what data is my service collecting? How and when is this data being used? Who has access to this data and who owns it? And how do we keep it secure? As service designers working with data on a daily basis, we want to raise awareness of the value of data to services. And we want to discuss fundamental questions around what happens to that data. This talk was held at Service Lab London on 19 October 2016 by Maria Izquierdo and Martin Jordan.

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A hegemony of all of big techs built on the collective data they possess and the power that the algorithms on this data can exert. Neither decentralizing nor centralizing, but perhaps forming a backbone or "Main street". This venture is something that could conceivably be worth trillions and advance the economic state of the world by an order of magnitude. No one would want to be left out of that, even if they couldn't be in charge of it themselves.

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Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change. Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software. Presented at OSCON Portland July 18, 2019

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Companies that understand how to apply AI will scale and win their respective markets over the next decade. That said, delivering on this promise and managing machine learning projects is much harder than most people anticpate. Many organizations hire teams of PhDs and data scientists, then fail to ship products that move business metrics. The root cause is often a lack of product strategy for AI, or the failure to adapt their product development processes to the needs of machine learning systems. This talk will cover some of the common ways machine learning fails in practice, the tactical responsibilities of AI product managers, and how to approach product strategy for AI. Peter Skomoroch, former Head of Data Products at Workday and LinkedIn, will describe how you can navigate these challenges to ship metric moving AI products that matter to your business. Peter will provide practical advice on: * The role of an AI Product Manager * How to evaluate and prioritize your AI projects * The ways AI product management differs from traditional product management * Bridging the worlds of design and machine learning * Making trade offs between data quality and other business metrics

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Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers. Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence. This talk was presented at the 2019 Strata Data Conference in London. Topics include: Who defines the data vision and roadmap in your organization? Who is accountable for building and expanding your competitive moat? Investing in foundational data infrastructure, training, logging, and tools Fostering executive support for exploration and innovation, including user-facing data product and algorithm development How to evaluate new machine intelligence projects and develop a portfolio that delivers How AI product management differs from traditional product management How to bridge the worlds of design and machine learning to get to product-market fit Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives

O'Reilly Strata: Distilling Data Exhaust

Talk from the first O'Reilly Strata, Feb 2011. Learn how to leverage data exhaust, the digital byproduct of our online activities, to solve problems and discover insights about the world around you. We will walk through a real world example which combines several datasets and statistical techniques to discover insights and make predictions about attendees at O'Reilly Strata. Includes a preview of some of the technology behind LinkedIn Skills, which I launched in a Keynote with DJ Patil the following day. Video: http://blip.tv/oreilly-promos/distilling-data-exhaust-4780870

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Endorsements are a one-click system to recognize someone for their skills and expertise on LinkedIn, the largest professional online social network. This is one of the latest “data features” in LinkedIn’s portfolio, and the endorsement ecosystem generates a large graph of reputation signals and viral user activity. In this talk, we’ll examine the practical aspects of building a data feature like Endorsements. We’ll talk about marrying product design and data, deep diving into several of the lessons we’ve learned along the way - all using skills & endorsements as an empirical case study. We’ll include technical detail on our approaches and how we combine crowdsourcing, machine learning, and large scale distributed systems to recommend topics to users.

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Practical problem solving with data involves more than just visualization or applying the latest machine learning techniques. Intuition, domain knowledge, and reasonable approximations can mean the difference between a successful model and a catastrophic failure. We’ll dive into some best practices I’ve extracted from solving real world problems like computing trending topics, cleaning election data, and ranking experts on social networks. New analysts or engineers are often lost when textbook approaches fail on real world data. Drawing inspiration from problem solving techniques in mathematics and physics, we will walk through examples that illustrate how come up with creative solutions and solve problems with big data.

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LinkedIn is the premiere professional social network with over 60 million users and a new user joining every second. One of LinkedIn's strategic advantages is their unique data. While most organizations consider data as a service function, LinkedIn considers data a cornerstone of their product portfolio. To rapidly develop these products LinkedIn leverages a number of technologies including open source, 3rd party solutions, and some we've had to invent along the way. This LinkedIn talk at the NYC Hadoop Meetup held 3/18 at ContextWeb focused on best practices for quickly uncovering patterns, visualizing trends, and generating actionable insights from large datasets.

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