Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Speaker slides & video

Presentation slides will be made available after the session has concluded and the speaker has given us the files. Check back if you don't see the file you're looking for—it might be available later! (However, please note some speakers choose not to share their presentations.)

Jeroen Janssens (Data Science Workshops B.V.)
"Anyone who does not have the command line at their beck and call is really missing something," tweeted Tim O'Reilly when Jeroen Janssens's Data Science at the Command Line was recently made available online for free. Join Jeroen to learn what you're missing out on if you're not applying the command line and many of its power tools to typical data science problems.
Moty Fania (Intel)
Moty Fania explains how Intel implemented an AI inference platform to enable internal visual inspection use cases and shares lessons learned along the way. The platform is based on open source technologies and was designed for real-time streaming and online actuation.
Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera)
Using Customer 360 and the IoT as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Flink, Kudu, Spark Streaming, and Spark SQL and modern storage engines to enable new forms of data processing and analytics.
Wataru Yukawa (LINE)
LINE—one of the most popular messaging applications in Asia—offers many services, such as its news application. These services sometimes depend on real-time processing. Wataru Yukawa offers an overview of LINE's web tracking system, which consists of the JavaScript SDK, NGINX Fluentd, Kafka, Elasticsearch, and Hadoop, and explains how it helps with batch and real-time processing.
Mick Hollison (Cloudera), Sven Löffler (Deutsche Telekom), Robert Neumann (Ultra Tendency)
What happens when you combine near-limitless data with on-demand access to powerful analytics and compute? For Deutsche Telekom, the results have been transformative. Mick Hollison, Sven Löffler, and Robert Neumann explain how Deutsche Telekom is harnessing machine learning and analytics in the cloud to build Europe’s largest and most powerful IoT data marketplace.
Eva Kaili (European Parliament | The Science and Technology Options Assessment Panel)
Keynote with Eva Kaili
Aurélien Géron (Kiwisoft)
Convolutional neural networks (CNN) can now complete many computer vision tasks with superhuman ability. This is will have a large impact on manufacturing, by improving anomaly detection, product classification, analytics, and more. Aurélien Géron details common CNN architectures, explains how they can be applied to manufacturing, and covers potential challenges along the way.
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.
Nanda Vijaydev (BlueData), Thomas Phelan (BlueData)
In the past, you needed a high-end proprietary stack for advanced machine learning, but today, you can use open source machine learning and deep learning algorithms available with distributed computing technologies like Apache Spark and GPUs. Nanda Vijaydev and Thomas Phelan demonstrate how to deploy a TensorFlow and Spark with NVIDIA CUDA stack on Docker containers in a multitenant environment.
Mathew Salvaris (Microsoft), Miguel Gonzalez-Fierro (Microsoft), Ilia Karmanov (Microsoft)
Mathew Salvaris, Miguel Gonzalez-Fierro, and Ilia Karmanov offer a comparison of two platforms for running distributed deep learning training in the cloud, using a ResNet network trained on the ImageNet dataset as an example. You'll examine the performance of each as the number of nodes scales and learn some tips and tricks as well as some pitfalls to watch out for.
Mark Donsky (Cloudera), Syed Rafice (Cloudera)
In May 2018, the General Data Protection Regulation (GDPR) goes into effect for firms doing business in the EU, but many companies aren't prepared for the strict regulation or fines for noncompliance (up to €20 million or 4% of global annual revenue). Mark Donsky and Syed Rafice outline the capabilities your data environment needs to simplify compliance with GDPR and future regulations.
Kevin Sigliano (IE Business School )
Financial and consumer ROI demands that business leaders understand the drivers and dynamics of digital transformation and big data. Kevin Sigliano explains why disrupting value propositions and continuous innovation are critical if you wish to dramatically improve the way your company engages customers and creates value and maximize financial results.
Dean Wampler (Lightbend)
Streaming data systems, so called fast data, promise accelerated access to information, leading to new innovations and competitive advantages. But they aren't just faster versions of big data. They force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. Dean Wampler outlines what you need to know to exploit fast data successfully.
Federico Leven (ReactoData)
The apparent difficulty of managing Hadoop compared to more traditional and proprietary data products makes some companies wary of the Hadoop ecosystem, but managing security is becoming more accessible in the Hadoop space, particularly in the Cloudera stack. Federico Leven offers an overview of an end-to-end security deployment on Hadoop and the data and security governance policies implemented.
In the past year, British Telecom has added a streaming network analytics use case to its multitenant data platform. Phillip Radley demonstrates how the solution works and explains how it delivers better broadband and TV services, using Kafka and Spark on YARN and HDFS encryption.
Thomas Phelan (BlueData)
Recent headline-grabbing data breaches demonstrate that protecting data is essential for every enterprise. The best-of-breed approach for big data is HDFS configured with Transparent Data Encryption (TDE), but TDE can be difficult to configure and manage—issues that are only compounded when running on Docker containers. Thomas Phelan discusses these challenges and explains how to overcome them.
Hollie Lubbock (Fjord), Jivan Virdee (Fjord)
Data has opened up huge possibilities for analyzing and customizing services. However, although we can now manage experiences to dynamically target audiences and respond immediately, context is often missing. Hollie Lubbock and Jivan Virdee share a practical approach to discovering the reasons behind the data patterns you see and help you decide what level of personalized service to create.
Jean-François Puget (IBM Analytics)
On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value? Jean-François Puget demonstrates the vital role of human context in answering those questions.
Ran Taig (Dell), Omer Sagi (Dell)
DevOps and QA engineers spend a significant amount of time investigating reoccurring issues. These issues are often represented by large configuration and log files, so the process of investigating whether two issues are duplicates can be a very tedious task. Ran Taig and Omer Sagi outline a solution that leverages NLP and machine learning algorithms to automatically identify duplicate issues.
Han Yang (Cisco Systems)
Han Yang explains how Cisco is leveraging big data and analytics and details how the company is helping customers to incorporate data sources from the internet of things and deploy machine learning at the edge and at the enterprise.
Alison Howard (Microsoft)
May 25, the day the GDPR goes into effect, is an important milestone for data protection in the EU and elsewhere, but the journey to GDPR compliance neither begins nor ends there. Alison Howard explains how Microsoft, one of the world’s largest companies, with operations across the EU and around the globe, has prepared for May 25 and beyond.
Jason Bell (MastodonC)
Jason Bell offers an overview of a self-learning knowledge system that uses Apache Kafka and Deeplearning4j to accept data, apply training to a neural network, and output predictions. Jason covers the system design and the rationale behind it and the implications of using a streaming data with deep learning and artificial intelligence.
Calum Murray (Intuit)
Machine learning-based applications are becoming the new norm. Calum Murray shares five use cases at Intuit that use the data of over 60 million users to create delightful experiences for customers by solving repetitive tasks, freeing them up to spend time more productively or solving very complex tasks with simplicity and elegance.
Christina Erlwein-Sayer (OptiRisk Systems)
Christina Erlwein-Sayer explains how to enhance the modeling and forecasting of sovereign bond spreads by considering quantitative information gained from macroeconomic news sentiment, using a number of large news analytics datasets.
Radhika Dutt (Radical Product), Geordie Kaytes (Fresh Tilled Soil), Nidhi Aggarwal (Radical Product)
These days it’s easy for companies to say, "We measure everything!” The problem is, most popular metrics may not be appropriate or relevant for your business. Measurement isn’t free and should be done strategically. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal explain how to align measurement with your product strategy so you can measure what matters for your business.
Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud.
Eran Avidan (Intel)
Deep learning is revolutionizing many domains within computer vision, but doing real-time analysis is challenging. Eran Avidan offers an overview of a novel architecture based on Redis, Docker, and TensorFlow that enables real-time analysis of high-resolution streaming video.
Aljoscha Krettek (data Artisans)
Aljoscha Krettek offers an overview of the modern stream processing space, details the challenges posed by stateful and event-time-aware stream processing, and shares core archetypes ("application blueprints”) for stream processing drawn from real-world use cases with Apache Flink.
Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Huawei)
Heitor Murilo Gomes and Albert Bifet offer an overview of StreamDM, a real-time analytics open source software library built on top of Spark Streaming, developed at Huawei's Noah’s Ark Lab and Télécom ParisTech.
Pierre Romera (International Consortium of Investigative Journalists (ICIJ))
Last November, the International Consortium of Investigative Journalists (ICIJ) published the Paradise Papers, a yearlong investigation on the offshore dealings of multinational companies and the wealthy. Pierre Romera offers a behind-the-scenes look into the process and explores the challenges in handling 1.4 TB of data and making it available securely to journalists all over the world.
mao baolong (, Yiran Wu (, Yupeng Fu (Alluxio)
Mao Baolong, Yiran Wu, and Yupeng Fu explain how uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average.
Sergey Ermolin (Intel), Olga Ermolin (MLS Listings)
Aggregation of geospecific real estate databases results in duplicate entries for properties located near geographical boundaries. Sergey Ermolin and Olga Ermolin detail an approach for identifying duplicate entries via the analysis of images that accompany real estate listings that leverages a transfer learning Siamese architecture based on VGG-16 CNN topology.
Jason Heo (Naver), Dooyong Kim (Navercorp) is the largest search engine in Korea, with a 70% share of the Korean search market, and it handles billions of pages and events everyday. Jason Heo and Dooyong Kim offer an overview of Naver's web analytics system, built with Druid.
Naghman Waheed (Monsanto), Brian Arnold (Monsanto)
There are a number of tools that make it easy to implement a data lake. However, most lack the essential features that prevent your data lake from turning into a data swamp. Naghman Waheed and Brian Arnold offer an overview of Monsanto's Data Historian platform, which can ingest, store, and access datasets without compromising ease of use, governance, or security.
Erin Recachinas (Zoomdata)
The value of real-time streaming analytics with historical data is immense. Big data application Zoomdata updates historical dashboards in real time without complex reaggregations, but streaming in the age of the IoT requires handling of data in volumes not seen in traditional feeds. Erin Recachinas explains how Zoomdata moved to a scalable microservice architecture for streaming sources.