Strata Data Conference

Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j

Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j

December 7, 2019

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.
Machine learning at Intuit: Five delightful use cases

Machine learning at Intuit: Five delightful use cases

December 7, 2019

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.
Machine learning platform lifecycle management

Machine learning platform lifecycle management

December 7, 2019

A machine learning platform is not just the sum of its parts; the key is how it supports the model lifecycle end to end. Hope Wang explains how to manage various artifacts and their associations, automate deployment to support the lifecycle of a model, and build a cohesive machine learning platform.
Machine-learned model quality monitoring in fast data and streaming applications

Machine-learned model quality monitoring in fast data and streaming applications

December 7, 2019

Most machine learning algorithms are designed to work on stationary data, but real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Emre Velipasaoglu reviews monitoring methods, focusing on their applicability in fast data and streaming applications.
Macroeconomic news sentiment: Enhanced risk assessment for sovereign bond spreads

Macroeconomic news sentiment: Enhanced risk assessment for sovereign bond spreads

December 7, 2019

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.
Measure what matters: How your measurement strategy can reduce opex

Measure what matters: How your measurement strategy can reduce opex

December 6, 2019

These days its easy for companies to say, "We measure everything! The problem is, most popular metrics may not be appropriate or relevant for your business. Measurement isnt 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.
Multi-data center and multitenant durable messaging with Apache Pulsar

Multi-data center and multitenant durable messaging with Apache Pulsar

December 6, 2019

Ivan Kelly offers an overview of Apache Pulsar, a durable, distributed messaging system, underpinned by Apache BookKeeper, that provides the enterprise features necessary to guarantee that your data is where is should be and only accessible by those who should have access.
Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench

Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench

December 6, 2019

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.
Practical advice for driving down the cost of cloud big data platforms

Practical advice for driving down the cost of cloud big data platforms

December 6, 2019

Big data and cloud deployments return huge benefits in flexibility and economics but can also result in runaway costs and failed projects. Drawing on his production experience, Christopher Royles shares tips and best practices for determining initial sizing, strategic planning, and longer-term operation, helping you deliver an efficient platform, reduce costs, and implement a successful project.
Putting AI to work for business: It's a journey. (sponsored by IBM)

Putting AI to work for business: It's a journey. (sponsored by IBM)

December 6, 2019

What was once science fiction has now become reality as multiple AI consumer-based solutions have hit the market over last few years. In turn, consumers have become more comfortable interacting with AI. But has AI really lived up to the hype? For consumers, perhaps not yet. However, AI for business is a different (and more valuable) animal. Carlo Appugliese details how business can put AI to work.
Real-time deep learning on video streams

Real-time deep learning on video streams

December 6, 2019

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.
Rendezvous with AI

Rendezvous with AI

December 6, 2019

Ted Dunning offers an overview of the rendezvous architecture, which is geared to deal with much of the complexity involved in deploying models to production, thus allowing more time to be spent thinking and doing real data science. Ted covers the ideas behind the architecture, practical scenarios, and advantages and disadvantages of the architecture.
Running data analytic workloads in the cloud

Running data analytic workloads in the cloud

December 6, 2019

Vinithra Varadharajan, Jason Wang, Eugene Fratkin, and Mael Ropars detail new paradigms to effectively run production-level pipelines with minimal operational overhead. Join in to learn how to remove barriers to data discovery, metadata sharing, and access control.
Scaling data science (teams and technologies)

Scaling data science (teams and technologies)

December 6, 2019

Cox Automotive is the worlds largest automotive service organization, which means it can combine data from across the entire vehicle lifecycle. Cox is on a journey to turn this data into insights. David Asboth and Shaun McGirr share their experience building up a data science team at Cox and scaling the company's data science process from laptop to Hadoop cluster.
Scaling the AI hierarchy of needs with TensorFlow, Spark, and Hops

Scaling the AI hierarchy of needs with TensorFlow, Spark, and Hops

December 5, 2019

Distributed deep learning can increase the productivity of AI practitioners and reduce time to market for training models. Hadoop can fulfill a crucial role as a unified feature store and resource management platform for distributed deep learning. Jim Dowling offers an introduction to writing distributed DL applications, covering TensorFlow and Apache Spark frameworks that make distribution easy.
Stream processing for the practitioner: Blueprints for common stream processing use cases with Apache Flink

Stream processing for the practitioner: Blueprints for common stream processing use cases with Apache Flink

December 5, 2019

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.
StreamDM: Advanced data science with Spark Streaming

StreamDM: Advanced data science with Spark Streaming

December 5, 2019

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 Noahs Ark Lab and Tlcom ParisTech.
The cloud is expensive, so build your own redundant Hadoop clusters.

The cloud is expensive, so build your own redundant Hadoop clusters.

December 5, 2019

Criteo has a production cluster of 2K nodes running over 300K jobs a day in the company's own data centers. These clusters were meant to provide a redundant solution to Criteo's storage and compute needs. Stuart Pook offers an overview of the project, shares challenges and lessons learned, and discusses Criteo's progress in building another cluster to survive the loss of a full DC.
The Paradise Papers: Behind the scenes with the ICIJ

The Paradise Papers: Behind the scenes with the ICIJ

December 5, 2019

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.
The ultimate data scientist's playground: Building a multipetabyte analytic infrastructure for cyber defense

The ultimate data scientist's playground: Building a multipetabyte analytic infrastructure for cyber defense

December 5, 2019

Lee Blum offers an overview of Verint's large-scale cyber-defense system built to serve its data scientists with versatile analytic operations on petabytes of data and trillions of records, covering the company's extremely challenging use case, decision considerations, major design challenges, tips and tricks, and the systems overall results.
Understanding Spark tuning with auto-tuning; or, Magical spells to stop your pager going off at 2:00am

Understanding Spark tuning with auto-tuning; or, Magical spells to stop your pager going off at 2:00am

December 5, 2019

Apache Spark is an amazing distributed system, but part of the bargain we've made with the infrastructure deamons involves providing the correct set of magic numbers (aka tuning) or our jobs may be eaten by Cthulhu. Holden Karau, Rachel Warren, and Anya Bida explore auto-tuning jobs using systems like Apache BEAM, Mahout, and internal Spark ML jobs as workloads.
Using Alluxio as a fault-tolerant pluggable optimization component of JD.com's compute frameworks

Using Alluxio as a fault-tolerant pluggable optimization component of JD.com's compute frameworks

December 4, 2019

Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com 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.
Using Siamese CNNs for removing duplicate entries from real estate listing databases

Using Siamese CNNs for removing duplicate entries from real estate listing databases

December 4, 2019

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.
Web analytics at scale with Druid at Naver

Web analytics at scale with Druid at Naver

December 4, 2019

Naver.com 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.
You call it data lake; we call it Data Historian.

You call it data lake; we call it Data Historian.

December 4, 2019

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.
Youre doing it wrong: How Zoomdata rearchitected streaming

Youre doing it wrong: How Zoomdata rearchitected streaming

December 4, 2019

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.
20 Netflix-style principles and practices to get the most out of your data platform

20 Netflix-style principles and practices to get the most out of your data platform

December 1, 2019

Kurt Brown explains how to get the most out of your data infrastructure with 20 principles and practices used at Netflix. Kurt covers each in detail and explores how they relate to the technologies used at Netflix, including S3, Spark, Presto, Druid, R, Python, and Jupyter.
20 Netflix-style principles and practices to get the most out of your data platform

20 Netflix-style principles and practices to get the most out of your data platform

December 1, 2019

Kurt Brown explains how to get the most out of your data infrastructure with 20 principles and practices used at Netflix. Kurt covers each in detail and explores how they relate to the technologies used at Netflix, including S3, Spark, Presto, Druid, R, Python, and Jupyter.