[TALK]@Telematika

Talks & Presentations Collection

Building practical AI systems
October 26, 2019

Building practical AI systems

As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems solving real-world problems for hundreds of millions of users. Drawing on specific examples, Adam reveals some of the techniques he uses to maximize the impact of the AI technologies he employs.

Can deep neural networks save your neural network? Artificial intelligence, sensors, and strokes

Can deep neural networks save your neural network? Artificial intelligence, sensors, and strokes

October 26, 2019

Each year, 15 million people suffer strokes, and at least a fifth of those are due to atrial fibrillation, the most common heart arrhythmia. Brandon Ballinger reports on a collaboration between UCSF cardiologists and ex-Google data scientists that detects atrial fibrillation with deep learning.
Can you afford to drop ACID? Understanding real-world SQL requirements in the big data era

Can you afford to drop ACID? Understanding real-world SQL requirements in the big data era

October 26, 2019

Hadoop can bring great value to businesses but also big headaches. Some solutions that provide SQL access to Hadoop data mean changing your business processes to overcome limitations in the technologies. Emma McGrattan explains how users can unlock tremendous business value through SQL-driven Hadoop solutions. Emma outlines what should be on your checklist and the pitfalls to avoid.
Connected brains

Connected brains

October 26, 2019

Joseph Sirosh offers a fascinating look into how brains connected with sensors to the cloud and machine learning could revolutionize a field of medicine.
Data science for good means designing for people: Part 1

Data science for good means designing for people: Part 1

October 26, 2019

So many of the data projects making headlinesfrom a new app for finding public services to a new probabilistic model for predicting weather patterns for subsistence farmersare great accomplishments but dont seem to have end users in mind. Discover how organizations are designing with, not for, people, accounting for what drives them in order to make long-lasting impact.
Data science for good means designing for people: Part 2

Data science for good means designing for people: Part 2

October 26, 2019

So many of the data projects making headlinesfrom a new app for finding public services to a new probabilistic model for predicting weather patterns for subsistence farmersare great accomplishments but dont seem to have end users in mind. Discover how organizations are designing with, not for, people, accounting for what drives them in order to make long-lasting impact.
Delivering "DARPA hard"

Delivering "DARPA hard"

October 26, 2019

The Defense Advanced Research Projects Agency (DARPA) is synonymous with transformational change, developing the seeming impossible into the practical. Matthew van Adelsberg demonstrates how collaborative teams of SMEs, data scientists, and engineers have been organized to achieve DARPA hard results for nearly a decade and offers insights into how companies can do the same.
Delivering information in context

Delivering information in context

October 26, 2019

Pivotals Ian Andrews explores why delivering information in context is the key to competitive differentiation in the digital economy.
Designing a scalable real-time data platform using Akka, Spark Streaming, and Kafka

Designing a scalable real-time data platform using Akka, Spark Streaming, and Kafka

October 26, 2019

Alex Silva outlines the implementation of a real-time analytics platform using microservices and a Scala stack that includes Kafka, Spark Streaming, Spray, and Akka. This infrastructure can process vast amounts of streaming data, ranging from video events to clickstreams and logs. The result is a powerful real-time data pipeline capable of flexible data ingestion and fast analysis.
Developing a big data business strategy

Developing a big data business strategy

October 25, 2019

Organizations do not need a big data strategy. They need a business strategy that incorporates big data. Most organizations lack a roadmap for using big data to uncover new business opportunities. Bill Schmarzo explains how to explore, justify, and plan big data projects with business management.
Docker for data scientists

Docker for data scientists

October 25, 2019

Data scientists inhabit such an ever-changing landscape of languages, packages, and frameworks that it can be easy to succumb to tool fatigue. If this sounds familiar, you may have missed the increasing popularity of Linux containers in the DevOps world, in particular Docker. Michelangelo D'Agostino demonstrates why Docker deserves a place in every data scientists toolkit.
Driving the on-demand economy with predictive analytics

Driving the on-demand economy with predictive analytics

October 25, 2019

The next evolution in the on-demand economy is in predictive analytics fueled by live streams of datain effect knowing what customers want before they do. Eric Frenkiel explains how a real-time trinity of technologiesKafka, Spark, and MemSQLis enabling Uber and others to power their own revolutions with predictive apps and analytics.
Embeddable data transformation for real-time streams

Embeddable data transformation for real-time streams

October 25, 2019

Real-time analysis starts with transforming raw data into structured records. Typically this is done with bespoke business logic custom written for each use case. Joey Echeverria presents a configuration-based, reusable library for data transformation that can be embedded in real-time stream-processing systems and demonstrates its real-world use cases with Apache Kafka and Apache Hadoop.
Empowering business users to lead with data

Empowering business users to lead with data

October 25, 2019

The most valuable people in your organization combine business acumen with data savviness. But these data heroes are rare. Denise McInerney describes how she has empowered business users at Intuit to make better decisions with data and explains how you can do the same thing in your organization.
Fast data made easy with Apache Kafka and Apache Kudu (incubating)

Fast data made easy with Apache Kafka and Apache Kudu (incubating)

October 25, 2019

Ted Malaska and Jeff Holoman explain how to go from zero to full-on time series and mutable-profile systems in 40 minutes. Ted and Jeff cover code examples of ingestion from Kafka and Spark Streaming and access through SQL, Spark, and Spark SQL to explore the underlying theories and design patterns that will be common for most solutions with Kudu.
Faster conclusions using in-memory columnar SQL and machine learning

Faster conclusions using in-memory columnar SQL and machine learning

October 25, 2019

Hadoops traditional batch technologies are quickly being supplanted by in-memory columnar execution to drive faster data-to-value. Wes McKinney and Jacques Nadeau provide an overview of in-memory columnar execution, survey key related technologies, including Kudu, Ibis, Impala, and Drill, and cover a sample use case using Ibis in conjunction with Apache Drill to deliver real-time conclusions.
Filling the data lake

Filling the data lake

October 25, 2019

A major challenge in todays world of big data is getting data into the data lake in a simple, automated way. Coding scripts for disparate sources is time consuming and difficult to manage. Developers need a process that supports disparate sources by detecting and passing metadata automatically. Chuck Yarbrough and Mark Burnette explain how to simplify and automate your data ingestion process.
Hadoop in the cloud: Good fit or round peg in a square hole?

Hadoop in the cloud: Good fit or round peg in a square hole?

October 25, 2019

Thomas Phelan and Joel Baxter investigate the advantages and disadvantages of running specific Hadoop workloads in different infrastructure environments. Thomas and Joel then provide a set of rules to help users evaluate big data runtime environments and deployment options to determine which is best suited for a given application.
High-performance clickstream analytics with Apache Phoenix and HBase

High-performance clickstream analytics with Apache Phoenix and HBase

October 25, 2019

Traditional data-warehousing techniques are sometimes limited by the scalability of the implementation tools themselves. Arun Thangamani explains how the advanced architectural approaches by tools like Apache Phoenix and HBase allow new, highly scalable live-analytics solutions using the same traditional techniques and showcases a successful implementation at CDK.
How the oil and gas industry is igniting a spark with information fusion and metadata analytics

How the oil and gas industry is igniting a spark with information fusion and metadata analytics

October 24, 2019

Oil and gas organizations are at the forefront of big data, adopting technologies such as Hadoop and Spark to develop next-generation fusion systems. Brian Clark and Marco Ippolito introduce a case study from CGG, a builder of common data models to drive analytics of sensor data and associated metadata from fast-changing big data streams, to show how to derive richer value from big data assets.
How to build a successful data lake

How to build a successful data lake

October 24, 2019

It is fashionable today to declare doom and gloom for the data lake. Alex Gorelik discusses best practices for Hadoop data lake success and provides real-world examples of successful data lake implementations in a non-vendor-specific talk.
How to make analytic operations look more like DevOps: Lessons learned moving machine-learning algorithms to production environments

How to make analytic operations look more like DevOps: Lessons learned moving machine-learning algorithms to production environments

October 24, 2019

There is a big difference between running a machine-learning algorithm manually from time to time and building a production system that runs thousands of machine-learning algorithms each day on petabytes of data, while also dealing with all the edge cases that arise. Robert Grossman discusses some of the lessons learned when building such a system and explores the tools that made the job easier.
How to turn your house into a robot: An adaptive-learning algorithm for the Internet of Things

How to turn your house into a robot: An adaptive-learning algorithm for the Internet of Things

October 24, 2019

Modern houses and robots have a lot in common. Both have a lot of sensors and have to make a lot of decisions. However, unlike houses, robots adapt and perform helpful tasks. Brandon Rohrer details an algorithm specifically designed to help houses, buildings, roads, and stores learn to actively help the people that use them.
How to use your data science team: Becoming a data-driven organization

How to use your data science team: Becoming a data-driven organization

October 24, 2019

Youve decided you need data scientists. You know who to hire. Now, what do you do with them? Yael Garten offers examples of how companies like LinkedIn use data to make business and product decisions. Yael reviews the spectrum of data science, and discusses the culture, process and tools needed to transform companies into data-driven organizations.
In search of database nirvana: The challenges of delivering HTAP

In search of database nirvana: The challenges of delivering HTAP

October 24, 2019

Companies are looking for a single database engine that can address all their varied needsfrom transactional to analytical workloads, against structured, semistructured, and unstructured data, leveraging graph databases, document stores, text search engines, column stores, key value stores, and wide column stores. Rohit Jain discusses the challenges one faces on the path to this nirvana.
Inside Cigna's big data journey

Inside Cigna's big data journey

October 24, 2019

How do you implement Apache Hadoop in a large healthcare company with a mature data-analysis infrastructure? Jeffrey Shmain and Mohammad Quraishi describe Cigna's journey toward big data and Hadoop, including an overview of new Hadoop capabilities like heterogeneous data integration and large-scale machine learning.
IoT in the enterprise: A look at Intel (IoT) Inside

IoT in the enterprise: A look at Intel (IoT) Inside

October 23, 2019

Moty Fania shares Intels IT experience implementing an on-premises big data IoT platform for internal use cases. This unique platform was built on top of several open source technologies and enables highly scalable stream analytics with a stack of algorithms such as multisensor change detection, anomaly detection, and more.
Large-scale product classification via text and image-based signals using a fusion of discriminative and deep learning-based classifiers

Large-scale product classification via text and image-based signals using a fusion of discriminative and deep learning-based classifiers

October 23, 2019

Typically, 810% of product URLs in ecommerce sites are misclassified. Sreeni Iyer and Anurag Bhardwaj discuss a machine-learning-based solution that relies on an innovative fusion of classifiers that are both text- and image-based, along with human touch to handle edge cases, to automatically classify product URLs according to a canonical taxonomic organization with a high F-score.