Schedule: Culture and Organization sessions

Creating and leading a successful ML strategy is an elegant orchestration of many components: master key ML concepts, operationalize ML workflow, prioritize highest-value projects, build a high-performing team, nurture strategic partnerships, align with the company’s mission, etc. Rossella Blatt Vital details insights and lessons learned in how to create and lead a flourishing ML practice. Read more.

Sourav Dey and Jakov Kucan walk you through the six steps of the Lean AI process and explain how it helps your ML engineers work as an an integrated part of your development and production teams. You'll get a hands-on example using real-world data, so you can get up and running with Docker and Orbyter and see firsthand how streamlined they can make your workflow. Read more.

The honeymoon era of data science is ending and accountability is coming. Not content to wait for results that may or may not arrive, successful data science leaders must deliver measurable impact on an increasing share of an enterprise’s KPIs. The speakers explore how leading organizations take a holistic approach to people, process, and technology to build a sustainable advantage. Read more.

The enterprise data management space has changed dramatically in recent years, and this has led to new challenges for organizations in creating successful data practices. Ted Malaska and Jonathan Seidman detail guidelines and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects. Read more.

With a microservice architecture, a data warehouse is the first place where all the data meets. It's supplied by many different data sources and used for many purposes—from near-online transactional processing (OLTP) to model fitting and real-time classifying. Evgeny Vinogradov details his experience in managing and scaling data for support of 20+ product teams. Read more.

While data science value is well recognized within tech, experience across industries shows that the ability to realize and measure business impact is not universal. A core issue is that data science programs face unique risks many leaders aren’t trained to hedge against. Brian Dalessandro addresses these risks and advocates for new ways to think about and manage data science programs. Read more.

As a steward for your enterprise’s data and digital transformation initiatives, you’re tasked with making the right choice. But before you can make those decisions, it’s important to understand what not to do when planning for your organization’s big data initiatives. Michael Stonebraker shares his top 10 big data blunders. Read more.

If, as a data scientist, you've wondered why it takes so long to deploy your model into production or, as an engineer, thought data scientists have no idea what they want, you're not alone. Join a lively discussion with industry veterans Ann Spencer, Paco Nathan, Amy Heineike, and Chris Wiggins to find best practices or insights on increasing collaboration when developing and deploying models. Read more.

Every organization wants to use data more effectively and as a weapon, but few succeed. Arup Nanda explores how Priceline started on this journey and how it was successful using different techniques and tools. Join in to learn how to streamline data assets, make it easier for end users, define KPIs, create value from data, and build sponsorships to build a data organization. Read more.

Mansplaining. Know it? Hate it? Want to make it go away? Sireesha Muppala, Shelbee Eigenbrode, and Emily Webber tackle the problem of men talking over or down to women and its impact on career progression for women. They also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds, examine ownership of the problem for women and men, and suggest helpful strategies. Read more.

Gayle Bieler explains how she built a thriving center for data science within a large, well-respected nonprofit research institute and shares some of its most impactful projects and best adventures to date, that have solved important national problems, improved local communities, and transformed research. Read more.

Automating decisions require a system to consider more than just a data-driven prediction. Real-world decisions require additional constraints and fuzzy objectives to ensure they're robust and consistent with business goals. Brian Keng takes a deep dive into how to leverage modern machine learning methods and traditional mathematical optimization techniques for decision automation. Read more.

This talk will explore some of the philosophy around the concept of explaining a model given the colloquial definition is partially recursive. It will cover the lens banking regulation places on this philosophical basis and expand into techniques used for these well governed aspects. Read more.

If you've ever been confused about what it takes to be a data scientist or curious about how companies recruit, train, and manage analytics resources, Usama Fayyad and Hamit Hamutcu are here to explore insights from the most comprehensive research effort to date on the data analytics profession and propose a framework for the standardization of roles and methods for assessing skills. Read more.

Jonathan Tudor and Ross Schalmo explore how GE Aviation made it a mission to implement self-service data. To ensure success beyond initial implementation of tools, the data engineering and analytics teams created initiatives to foster engagement from an ongoing partnership with each part of the business to the gamification of tagging data in a data catalog to forming a published dataset council. Read more.