Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Media, Marketing, Advertising sessions

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11:1511:55 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 7
Itai Yaffe (Nielsen)
At Nielsen Marketing Cloud, we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. To achieve that, we need to ingest billions of events per day into our big data stores and we need to do it in a scalable yet cost-efficient manner. In this talk, we will discuss how we continuously transform our data infrastructure to support these goals. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
In this talk you will learn how to use Spark NLP and Apache Spark to standardize semi-structured text. You will see how Indeed standardizes resume content at scale. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Mounia Lalmas (Spotify)
The aim of our mission is "to match fans and artists in a personal and relevant way". In this talk, Mounia will describe some of the (research) work we are doing to achieve this, from using machine learning to metric validation. She will describe works done in the context of Home, Search and Voice. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Maryam Jahanshahi (TapRecruit)
In this talk I will discuss exponential family embeddings, which are methods that extend the idea behind word embeddings to other data types. I will describe how we used dynamic embeddings to understand how data science skill-sets have transformed over the last 3 years using our large corpus of job descriptions. The key takeaway is that these models can enrich analysis of specialized datasets. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 7
Simona Meriam (Nielsen)
Ingesting billions of events per day into our big data stores we need to do it in a scalable, cost-efficient and consistent way. When working with Spark and Kafka the way you manage your consumer offsets has a major implication on data consistency. We will go in depths of the solution we ended up implementing and discuss the working process, the dos and don'ts that led us to its final design. Read more.
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11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Sophie Watson (Red Hat)
Identifying relevant documents quickly and efficiently enhances both user experience and business revenue every day. Sophie Watson demonstrates how to implement Learning to Rank algorithms and provides you with the information you need to implement your own successful ranking system. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Oliver Gindele (Datatonic)
The success of Deep Learning has reached the realm of structured data in the past few years where neural network have shown to improve the effectiveness and predictability of recommendation engines. This session will give a brief overview of such deep recommender systems and how they can be implemented in TensorFlow. Read more.