Schedule List ViewGrid View

What can we learn from a one-billion-person live poll of the internet? Andrew Montalenti explains how Parse.ly has gathered a unique dataset of news reading sessions of billions of devices, peaking at over two million sessions per minute on thousands of high-traffic news and information websites, and how the company uses this data to unearth the secrets behind online content.

David Talby, Alberto Andreotti, Stacy Ashworth, and Tawny Nichols outline a question-answering system for accurately extracting facts from free-text patient records and share best practices for training domain-specific deep learning NLP models. The solution is based on Spark NLP, an extension of Spark ML that provides state-of-the-art performance and accuracy for natural language understanding.

Many details go into building a big data system for speed, from determining a respectable latency until data access and where to store the data to solving multiregion problems—or even knowing just what data you have and where stream processing fits in. Mark Grover and Ted Malaska share challenges, best practices, and lessons learned doing big data processing and analytics at scale and at speed.

Temporal data is being produced in ever-greater quantity, but fortunately our time series capabilities are keeping pace. Jared Lander explores techniques for modeling time series, from traditional methods such as ARMA to more modern tools such as Prophet and machine learning models like XGBoost and neural nets. Along the way, Jared shares theory and code for training these models.

Drawing on a recent study of the analytics maturity level of large enterprises by the International Institute for Analytics, Bill Franks discusses how maturity varies by industry, shares key steps organizations can take to move up the maturity scale, and explains how the research correlates analytics maturity with a wide range of success metrics, including financial and reputational measures.

In EU households, heating and hot water alone account for 80% of energy usage. Cristobal Lowery and Marc Warner explain how future home energy management systems could improve their energy efficiency by predicting resident needs through utilities data, with a particular focus on the key data features, the need for data compression, and the data quality challenges.

Machine learning tools promise to help solve data curation problems. While the principles are well understood, the engineering details in configuring and deploying ML techniques are the biggest hurdle. Ihab Ilyas explains why leveraging data semantics and domain-specific knowledge is key in delivering the optimizations necessary for truly scalable ML curation solutions.

Building accurate machine learning models hinges on the quality of the data. Errors and anomalies get in the way of data scientists doing their best work. Archana Anandakrishnan explains how American Express created an automated, scalable system for measurement and management of data quality. The methods are modular and adaptable to any domain where accurate decisions from ML models are critical.

Emily Riederer explains how best practices from data science, open source, and open science can solve common business pain points. Using a case example from Capital One, Emily illustrates how designing empathetic analytical tools and fostering a vibrant InnerSource community are keys to developing reproducible and extensible business analysis.

Large financial institutions have many data science teams (e.g., those for fraud, credit risk, and marketing), each often using diverse set of tools to build predictive models. There are many challenges involved in productionizing these predictive AI models. Harish Doddi and Jerry Xu share challenges and lessons learned deploying AI models to production in large financial institutions.

Jeffrey Heer offers an overview of Vega and Vega-Lite—high-level declarative languages for interactive visualization that support exploratory data analysis, communication, and the development of new visualization tools.

Augmented reality opens a completely new lens on your data through which you see and accomplish amazing things. Bob Levy explains how to use simple Python scripts to leverage completely new plot types. You'll explore use cases revealing new insight into financial markets data as well as new ways of interacting with data that build trust in otherwise “black box” machine learning solutions.

Companies collect all kinds of data and use advanced tools and techniques to find insights, but they often fail in the last mile: communicating insights effectively to drive change. Brent Dykes discusses the power that stories wield over statistics and explores the art and science of data storytelling—an essential skill in today’s data economy.

Data visualization is supposed to be our map to information. However, contemporary charting techniques have a few shortcomings, including context reduction, hard numeric grasp, and perceptual dehumanization. Anna Nicanorova explains how augmented reality can solve these issues by presenting an intuitive and interactive environment for data exploration.

Gartner says 85%+ of big data projects will fail, despite the fact your company may have invested millions on engineering implementation. Why are customers and employees not engaging with these products and services? Brian O'Neill explains why a "people first, technology second" mission—a design strategy, in other words—enables the best UX and business outcomes possible.

Transfer learning allows data scientists to leverage insights from large labeled datasets. The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where little labeled data is available. Lars Hulstaert explains what transfer learning is and how it can boost your NLP or CV pipelines.

Moty Fania and Sergei Kom share their experience and lessons learned implementing an AI inference platform to enable internal visual inspection use cases. The platform is based on open source technologies and was designed for real-time, streaming, and online actuation.

Can the talent industry make the job search/match more relevant and personalized for a candidate by leveraging deep learning techniques? Guoqiong Song, Wenjing Zhan, and Jacob Eisinger demonstrate how to leverage distributed deep learning framework BigDL on Apache Spark to predict a candidate’s probability of applying to specific jobs based on their résumé.

Ash Munshi outlines a technique for labeling applications using runtime measurements of CPU, memory, and network I/O along with a deep neural network. This labeling groups the applications into buckets that have understandable characteristics, which can then be used to reason about the cluster and its performance.

In this auditory world, the human brain processes and reacts effortlessly to a variety of sounds. While many of us take this for granted, there are over 360 million in this world who are deaf or hard of hearing. Swetha Machanavajhala and Xiaoyong Zhu explain how to make the auditory world inclusive and meet the great demand in other sectors by applying deep learning on audio in Azure.

Jonathan Hung, Keqiu Hu, and Zhe Zhang offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other first-class citizens on Hadoop.

In order to train deep learning and machine learning models, you must leverage applications such as TensorFlow, MXNet, Caffe, and XGBoost. Wangda Tan discusses new features in Apache Hadoop 3.x to better support deep learning workloads and demonstrates how to run these applications on YARN.

Michelle Casbon demonstrates how to build a machine learning application with Kubeflow. Kubeflow makes it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and supports the full lifecycle of an ML product, including iteration via Jupyter notebooks. Join Michelle to find out what Kubeflow currently supports and the long-term vision for the project.

When we talk about microservices, we usually focus on the communication layer. In practice, data is the much harder and often overlooked problem. Splitting applications into independent units leads to increased complexity, such as structural and semantic changes, knowledge sharing, and data discovery. Join Alexey Kachayev to explore emerging technologies created to tackle these challenges.

Amandeep Khurana shares critical data management practices for easy and unified data access that meets security and regulatory compliance, helping you avoid the pitfalls that could lead to complex expensive architectures.

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.

Every new launch at Uber is vetted via robust A/B testing. Given the pace at which Uber operates, the metrics needed to assess the impact of experiments constantly evolve. Milene Darnis explains how the team built a scalable and self-serve platform that lets users plug in any metric to analyze.

Occhio Orsini offers an overview of Aetna's Data Fabric platform. Join in to learn the needs and desires that led to the creation of the advanced analytics platform, explore the platform's architecture, technology, and capabilities, and understand the key technologies and capabilities that made it possible to build a hybrid solution across on-premises and cloud-hosted data centers.

Lockheed Martin is a data-driven company with a massive variety and volume of data. To extract the most value from its information assets, the company is constantly exploring ways to enable effective self-service scenarios. Ramesh Krishnan and Steve Morgan discuss Lockheed Martin's journey into modern analytics and explore its analytics platform focused on leveraging AWS GovCloud.

JW Player—the world’s largest network-independent video platform, representing 5% of global internet video—provides on-demand recommendations as a service to thousands of media publishers. Nir Yungster and Kamil Sindi explain how the company is systematically improving model performance while navigating the many engineering challenges and unique needs of the diverse publishers it serves.

Stateful containers are a well-known anti-pattern, but the standard solution—managing state in a separate storage tier—is costly and complex. Recent developments have changed things dramatically for the better. In particular, you can now manage a high-performance software-defined-storage tier entirely in Kubernetes. Ted Dunning describes what's new and how it makes big data easier on Kubernetes.

Kaushik Deka and Ted Gibson share a large-scale optimization architecture in Spark for a consumer product portfolio optimization use case in retail banking. The architecture combines a simulator that distributes computation of complex real-world scenarios and a constraint optimizer that uses business rules as constraints to meet growth targets.

Financial service clients demand increased data-driven personalization, faster insight-based decisions, and multichannel real-time access. Tim Walpole details how organizations can deliver real-time, vendor-agnostic, personalized chat services and explores issues around security, privacy, legal sign-off, data compliance, and how the internet of things can be used as a delivery platform.

Is open source Apache Cassandra still relevant in an era of hosted cloud databases? Jonathan Ellis discusses Cassandra’s strengths and weaknesses relative to Amazon DynamoDB, Microsoft CosmosDB, and Google Cloud Spanner.

Recruit Group and NTT DATA Corporation have developed a platform based on a data hub, utilizing Apache Kafka. This platform can handle around 1 TB/day of application logs generated by a number of services in Recruit Group. Kenji Hayashida and Toru Sasaki share best practices for and lessons learned about topics such as schema evolution and network architecture.

Thomas Weise and Mark Grover explain how Lyft uses its streaming platform to detect and respond to anomalous events, using data science tools for machine learning and a process that allows for fast and predictable deployment.

The controller is the brain of Apache Kafka and is responsible for maintaining the consistency of the replicas. Jun Rao outlines the main data flow in the controller, then describes recent improvements to the controller that allow it to handle certain edge cases correctly and increase its performance, which allows for more partitions in a Kafka cluster.

Apache Pulsar is being used for an increasingly broad array of data ingestion tasks. When operating at scale, it's very important to ensure that the system can make use of all the available resources. Karthik Ramasamy and Matteo Merli share insights into the design decisions and the implementation techniques that allow Pulsar to achieve high performance with strong durability guarantees.

The StreamDM library provides the largest collection of data stream mining algorithms for Spark. Heitor Murilo Gomes and Albert Bifet explain how to use StreamDM and Structured Streaming to develop, apply, and evaluate learning models specially for nonstationary streams (i.e., those with concept drifts).

Timothy Spann leads a hands-on deep dive into using Apache MiniFi with Apache MXNet and other deep learning libraries on edge devices.

Comcast’s streaming data platform comprises ingest, transformation, and storage services in the public cloud, with Apache Atlas for data discovery and lineage. Barbara Eckman explains how Comcast recently integrated on-prem data sources, including traditional data warehouses and RDBMSs, which required its data governance strategy to include relational and JSON schemas in addition to Apache Avro.

Global heavy equipment manufacturer Komatsu is using IoT data to continuously monitor some of the largest mining equipment to ultimately improve mine performance and efficiencies. Shawn Terry details the company's data journey and explains how it is using advanced analytics and predictive modeling to drive insights on terabytes of IoT data from connected mining equipment.

Tao Huang, Mang Zhang, and 白冰 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.

PayPal is one of the biggest Kafka users in the industry; it manages and maintains over 40 production Kafka clusters in three geodistributed data centers and supports 400 billion Kafka messages a day. Kevin Lu, Maulin Vasavada, and Na Yang explore the management and monitoring PayPal applies to Kafka, from client-perceived statistics to configuration management, failover, and data loss auditing.

Have you ever tuned a Spark or MR job? If the answer is yes, you already know how difficult it is to tune more than hundred parameters to optimize the resources used. Manoj Kumar, Pralabh Kumar, and Arpan Agrawal offer an overview of TuneIn, an auto-tuning tool developed to minimize the resource usage of jobs. Experiments have shown up to a 50% reduction in resource usage.

Data scientists are hard to hire. But too often, companies struggle to find the right talent only to make avoidable mistakes that cause their best data scientists to leave. From org structure and leadership to tooling, infrastructure, and more, Michelangelo D'Agostino shares concrete (and inexpensive) tips for keeping your data scientists engaged, productive, and adding business value.

Bruno Faria explains how to identify the components and workflows in your current environment and shares best practices to migrate these workloads to AWS.

Joshua Laurito explores systems Squarespace built for acquiring and enforcing consistency on obtained data and for inferring conclusions from a company’s marketing and product initiatives. Joshua discusses the intricacies of gathering and evaluating marketing and user data, from raising awareness to driving purchases, and shares results of previous analyses.

Francesco Mucio tells the story of how Zalando went from an old-school BI company to an AI-driven company built on a solid data platform. Along the way, he shares what Zalando learned in the process and the challenges that still lie ahead.

One of the challenges of sports data analytics is how to deliver machine intelligence beyond a mere real-time monitoring tool. Yasuyuki Kataoka highlights various real-time machine learning models in both IndyCar and Tour de France, sharing real-time data processing architectures, machine learning models, and demonstrations that deliver meaningful insights for players and fans.

The Wikipedia community feels strongly that you shouldn’t have to provide personal information to participate in the free knowledge movement. Nuria Ruiz discusses the challenges that this strong privacy stance poses for the Wikimedia Foundation, including how it affects data collection, and details some creative workarounds that allow WMF to calculate metrics in a privacy-conscious way.

GDPR is more than another regulation to be handled by your back office. Enacting the GDPR's Data Subject Access Rights (DSAR) requires practical actions. Jean-Michel Franco outlines the practical steps to deploy governed data services.

Chiny Driscoll and Jawad Khan offer an overview of a solution by Cloudera and MetiStream that lets healthcare providers automate the extraction, processing, and analysis of clinical notes within an electronic health record in batch or real time, improving care, identifying errors, and recognizing efficiencies in billing and diagnoses.

GDPR asks us to rethink personal data systems—viewing UI/UX, consent management, and value-add data services through the eyes of subjects of the data. LaVonne Reimer explains why the opportunity in the $150B credit and risk industry is to deploy data governance technologies that balance the interests of individuals to control their own data with requirements for trusted data.

With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.

In order to become "AI ready," an organization not only has to provide the right technical infrastructure for data collection and processing but also must learn new skills. Mikio Braun highlights three pieces companies often miss when trying to become AI ready: making the connection between business problems and AI technology, implementing AI-driven development, and running AI-based projects.

Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. Join Brandy Freitas to develop context and vocabulary around data science topics to help build a culture of data within your organization.

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 shares what you need to know to exploit fast data successfully.

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.

Michelle Ufford shares some of the cool things Netflix is doing with data and the big bets the company is making on data infrastructure, covering workflow orchestration, machine learning, interactive notebooks, centralized alerting, event-based processing, platform intelligence, and more.

PostgreSQL is often regarded as the world’s most advanced open source database—and it’s on fire. Umur Cubukcu moves beyond the typical list of features in the next release to explore why so many new projects “just use Postgres” as their system of record (or system of engagement) at scale. Along the way, you’ll learn how PostgreSQL’s extension APIs are fueling innovations in relational databases.

Chris Fregly details a full-featured, open source end-to-end TensorFlow model training and deployment system, using the latest advancements with Kubernetes, TensorFlow, and GPUs.

Common wisdom dictates that we should never make assumptions, but assumptions are essential in the creation of statistical models. Jennifer Shin explores how assumptions fit into the creation of a statistical model, the pitfalls of applying a model to data without taking the underlying assumptions into account, and how to identify datasets where the model and its assumptions are applicable.

Operationalizing big data apps in a quick, reliable, and cost-effective manner remains a daunting task. Shivnath Babu and Madhusudan Tumma outline common problems and their causes and share best practices to find and fix these problems quickly and prevent such problems from happening in the first place.

There is a lot of buzz around data science and machine learning in the world today. Unfortunately, to truly innovate with data and advanced capabilities, organizations need to expand their focus beyond just a few specialists. Patrick Nussbaumer details how focusing on people can help improve analytic value and drive innovation.

Ajay Kulkarni explores the underlying changes that are characterizing the next wave of computing and shares several ways in which individual businesses and overall industries will be transformed.

Common wisdom dictates that we should never make assumptions, but assumptions are essential in the creation of statistical models. Jennifer Shin explores how assumptions fit into the creation of a statistical model, the pitfalls of applying a model to data without taking the underlying assumptions into account, and how to identify datasets where the model and its assumptions are applicable.

If your company isn’t good at analytics, it’s not ready for AI. Bob Bradley and Chad W. Jennings explain how the right data strategy can set you up for success in machine learning and artificial intelligence—the new ground for gaining competitive edge and creating business value. You'll then see an in-depth demonstration of Google technology from smart cities innovator Geotab.

Ian Swanson explores why and how data scientists and line-of-business leaders must treat AI as a team sport and explains what tools are needed to deploy models and applications that truly inform decision making.

Kyle Davis explains how Redis can be used for ingesting high-velocity data from large-scale platforms and IoT data collections as well as for storing and querying data using probabilistic data structures that trade some precision for both higher speed and lower storage requirements. Along the way, Kyle shares examples and a demo of the solution.

Machine learning has so far underperformed in time series prediction (slowness and overfitting), and classical methods are ineffective at capturing nonlinearity. Revant Nayar shares an alternative approach that is faster and more transparent and does not overfit. It can also pick up regime changes in the time series and systematically captures all the nonlinearity of a given dataset.

Arun Murugan and Jeff Miller detail how complex relationships are discovered and modeled to simplify analytics while keeping an Agile architecture for data acquisition. You’ll see how GE uses machine learning (powered by Io-Tahoe) in data discovery and profiling for data engineering of the development of a standard data model essential to enterprise use cases.

Data in most organizations today is massive, messy, and often found in silos. With so many sources to analyze, data engineers need to construct robust data pipelines using automation and minimize duplicate processes, as computation is costly for big data. David Huh shares strategies to construct data pipelines for machine learning, including one to reduce time to insight from weeks to hours.

By creating a collaborative and interactive analytic environment, a forward-thinking company may harness the best capabilities of its business analysts and data scientists to answer the company’s most pressing business questions. Deborah Reynolds and Kurt Muehmel explain how large enterprises can successfully put data at the core of everyday business decisions.

Antonio Fragoso explores the key aspects of implementing a natural language processing project within your organization and reveals the necessary steps for making it a success. Antonio focuses on how to leverage an iterative process that can pave the way toward building a successful product.

Program chairs Ben Lorica, Doug Cutting, and Alistair Croll welcome you to the second day of keynotes.

Amber Case outlines several methods that product designers and managers can use to improve everyday interactions through an understanding and application of sound design.

IBM Analytics’s Dinesh Nirmal solves school lunch and the struggle to keep ahead of regulations. With AI tech like deep learning and NLG, supplying meals to California’s kids leaps from enriching metadata for compliance to actionable insights for the business.

Machine learning and artificial intelligence are exciting technologies, but real value comes from marrying those capabilities with the right business problems. Hilary Mason explores the current state of these technologies, investigates what's coming next in applied machine learning, and explains how to identify and execute on the right business opportunities at the right time.

Data is the fuel for analytics and AI workloads, but the challenges in using it are constant. Ziya Ma discusses how recent innovations from Intel in high-capacity persistent memory and open source software are accelerating production-scale deployments, delivering breakthrough optimizations and faster insights to a wide range of opportunities in the digital enterprise.

Algorithms are increasingly arbiters of forgiveness. Julia Angwin discusses what she has learned about forgiveness in her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.

Cities all over the world are using data and analytics to optimize infrastructure, but city planners are often held back by outdated data gathering methods and legacy analysis tools. Chad Jennings details how Geotab, a leader in IoT fleet logistics, brought BigQuery's unique machine learning and geospatial capabilities to its existing datasets to deliver a more capable solution to city planners.

Have you ever dreamed you could read minds? Do telekinesis? Maybe fly a magic carpet by thought alone? Until now, these powers have existed only in the realm of imagination or, more recently, video, AR, and VR games. Join Amanda Pustilnik to learn how brain-based human-machine interfaces are beginning to offer these powers in near-commercially-viable forms.

Once, a company could live 60-70 years on the S&P 500. Now it averages 15 years. If companies were people, this would be an epidemic on par with the Black Plague. But the same things that dragged humanity out of that dark age can drag companies out of this one.

For most of us, our own mind is a black box—an all-powerful and utterly mysterious device that runs our lives for us, using rules and shortcuts of which we aren’t even aware. Jacob Ward reveals the relationship between the unconscious habits of our minds and the way that AI is poised to amplify them, alter them, maybe even reprogram them.

Gather before keynotes on Thursday morning to enjoy casual conversation while meeting fellow attendees. If one of your goals at Strata is to meet new people, this session will jumpstart your networking with other attendees.

Join Strata Business Summit speakers and attendees for a networking lunch on Thursday.

Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.