Schedule: Media and Advertising sessions

Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system. Read more.

Join Swasti Kakker, Manu Ram Pandit, and Vidya Ravivarma to explore what's offered by a flexible and scalable hosted data science platform at LinkedIn. It provides features to seamlessly develop in multiple languages, enforce developer best practices, governance policies, execute, visualize solutions, efficient knowledge management, and collaboration to improve developer productivity. Read more.

Imagine scaling metadata to an organization of 10,000 employees, 1M+ data assets, and an AI-enabled company that ships code to the site three times a day. Shirshanka Das and Mars Lan dive into LinkedIn’s metadata journey from a two-person back-office team to a central hub powering data discovery, AI productivity, and automatic data privacy. They reveal metadata strategies and the battle scars. Read more.

Fei Wang takes a deep dive into a case study for the CarGurus TV Attribution Model. You'll understand how you can leverage the creation of a causal inference model to calculate cost per acquisition (CPA) of TV spend and measure effectiveness when compared to CPA of digital performance marketing spend. Read more.

Criteo’s infrastructure provides the capacity and connectivity to host Criteo’s platform and applications. The evolution of this infrastructure is driven by the ability to forecast Criteo’s traffic demand. Hamlet Jesse Medina Ruiz explains how Criteo uses Bayesian dynamic time series models to accurately forecast its traffic load and optimize hardware resources across data centers. Read more.

Trill has been open-sourced, making the streaming engine behind services like the Bing Ads platform available for all to use and extend. James Terwilliger, Badrish Chandramouli, and Jonathan Goldstein dive into the history of and insights from streaming data at Microsoft. They demonstrate how its API can power complex application logic and the performance that gives the engine its name. Read more.

Organizations often work with sensitive information such as social security and credit card numbers. Although this data is stored in encrypted form, most analytical operations require data decryption for computation. This creates unwanted exposures to theft or unauthorized read by undesirables. Matt Carothers, Jignesh Patel, and Harry Tang explain how homomorphic encryption prevents fraud. Read more.

Using SAS, Python, and AWS SageMaker, Major League Baseball's (MLB's) data science team outlines how it predicts ticket purchasers’ likelihood to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service. Read more.

Machine learning infrastructure is key to the success of AI at scale in enterprises, with many challenges when you want to bring machine learning models to a production environment, given the legacy of the enterprise environment. Venkata Gunnu and Harish Doddi explore some key insights, what worked, what didn't work, and best practices that helped the data engineering and data science teams. Read more.

You're a SaaS company operating on a cloud infrastructure prior to the machine learning (ML) era and you need to successfully extend your existing infrastructure to leverage the power of ML. Jing Huang and Jessica Mong detail a case study with critical lessons from SurveyMonkey’s journey of expanding its ML capabilities with its rich data repo and hybrid cloud infrastructure. Read more.

Failures or issues in a product or service can negatively affect the business. Detecting issues in advance and recovering from them is crucial to keeping the business alive. Join Akshay Rai to learn more about LinkedIn's next-generation open source monitoring platform, an integrated solution for real-time alerting and collaborative analysis. Read more.

Mumin Ransom gives an overview of the data management and privacy challenges around automating ML model (re)deployments and stream-based inferencing at scale. Read more.