Sep 23–26, 2019

Schedule: Media and Advertising sessions

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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 03
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
In this two-days workshop, you will learn the different paradigms of recommendation systems and get introduced to the usage of deep-learning based approaches . By the end of the workshop, you will have enough practical hands-on knowledge to build, select, deploy and maintain a recommendation system for your problem. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 21/22
Swasti Kakker (LinkedIn), Manu Ram Pandit (LinkedIn), Vidya Ravivarma (LinkedIn)
Come hear about the infrastructure and features offered by flexible and scalable hosted data science platform at LinkedIn. The platform provides features to seamlessly develop in multiple languages, enforce developer best practices, governance policies, execute, visualize solutions, efficient knowledge management and collaboration that improve developer productivity. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 23/24
Shirshanka Das (LinkedIn), Mars Lan (LinkedIn)
How do you scale 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. We describe the journey of LinkedIn’s metadata from a two-person back-office team to a central hub powering data discovery, AI productivity and automatic data privacy. Different metadata strategies and our battle scars will be revealed! Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 08/10
Fei Wang (CarGurus), Michael Brautbar (CarGurus)
This session will present the case study for the CarGurus TV Attribution Model. Attendees will learn how the creation of a causal inference model can be leveraged to calculate cost per acquisition (CPA) of TV spend and measure effectiveness when compared to CPA of Digital Performance Marketing spend. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 12/14
Criteo’s infrastructure provides capacity and connectivity to host Criteo’s platform and applications. The evolution of our infrastructure is driven by the ability to forecast Criteo’s traffic demand. In this talk, we explain how Criteo uses Bayesian Dynamic time series models to accurately forecast its traffic load and optimize hardware resources across data centers. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 15/16
James Terwilliger (Microsoft Corporation), Badrish Chandramouli (Microsoft Research), Jonathan Goldstein (Microsoft Research)
Trill has been open-sourced, making the streaming engine behind services like the multi-billion-dollar Bing Ads platform available for all to use and extend. We give a brief history of streaming data at Microsoft and lessons learned. We then demonstrate how its API can power complex application logic, and the performance that gives the engine its name: a trillion events per day per node. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 1E 09
Matt Carothers (Cox Communications), Jignesh Patel (Cox Communications), Harry Tang (Cox Communication Inc)
Organizations often work with sensitive information such as social security number, and Credit card information. Although this data is stored in encrypted form, most analytical operations ranging from data analysis to advanced machine learning algorithms require data decryption for computation. This creates unwanted exposures to theft or unauthorized read by undesirables. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 08/10
Aaron Owen (Major League Baseball), Matthew Horton (Major League Baseball), Josh Hamilton (MLB)
Utilizing SAS, Python, and AWS Sagemaker, MLB’s data science team discusses how it predicts ticket purchasers’ likelihoods 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.
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11:20am12:00pm Thursday, September 26, 2019
Location: 1A 23/24
Jing Huang (SurveyMonkey), Jessica Mong (SurveyMonkey)
You are a SaaS company that operates on a cloud infra prior to the ML era. How do you successfully extend your existing infrastructure to leverage the power of ML? In this case study, you will learn critical lessons from SurveyMonkey’s journey of expanding its ML capabilities with its rich data repo and hybrid cloud infrastructure. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1E 14
Akshay Rai (Linkedin)
Failures or issues in a product or service can negatively affect the business. Detecting issues in advance and recovering from them is crucial to keep the business alive. Come, join us, to learn more about LinkedIn's next-generation open-source monitoring platform, an integrated solution for real-time alerting and collaborative analysis. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12/14
Andrew Leamon (Comcast), Wadkar Sameer (Comcast NBCUniversal)
And overview of the Data Management and privacy challenges around automating ML model (re)deployments and stream based inferencing at scale. Read more.

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