big data

Lessons Learned from the Migration to Apache Airflow

Lessons Learned from the Migration to Apache Airflow

February 26, 2020

Apache Airflow is an open-source tool for orchestrating complex workflows and data processing pipelines.In this talk, Radek Maciaszek will present his learnings from the migration of machine learning …
Deploying machine learning models on the edge

Deploying machine learning models on the edge

February 20, 2020

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.
Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data

Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data

February 19, 2020

Voiced-based AI continues to gain popularity among customers, businesses, and brands, but its important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Andreas Kaltenbrunner examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.
PacBot: Enabling Cloud Agility with Automated Compliance and Remediation

PacBot: Enabling Cloud Agility with Automated Compliance and Remediation

February 19, 2020

For T-Mobile, the purpose of public cloud is to accelerate time-to-value for customers, unlocking developer agility while protecting the business from critical security and compliance issues. This is …
Unlocking data capital with AI (sponsored by Dell)

Unlocking data capital with AI (sponsored by Dell)

February 17, 2020

As we look toward more demanding applications of artificial intelligence to unlock value from data, it's increasingly essential to develop a sustainable big data strategy and to efficiently scale artificial intelligence initiatives. Arash Ghazanfari covers the fundamental principles that need to be considered in order to achieve this goal.
Apache Hadoop 3.x state of the union and upgrade guidance

Apache Hadoop 3.x state of the union and upgrade guidance

February 16, 2020

Wangda Tan and Wei-Chiu Chuang outline the current status of Apache Hadoop community and dive into present and future of Hadoop 3.x. You'll get a peak at new features like erasure coding, GPU support, NameNode federation, Docker, long-running services support, powerful container placement constraints, data node disk balancing, etc. And they walk you through upgrade guidance from 2.x to 3.x.
Cisco Data Intelligence Platform (sponsored by Cisco)

Cisco Data Intelligence Platform (sponsored by Cisco)

February 16, 2020

Siva Sivakumar explains the Cisco Data Intelligence Platform (CDIP), which is a cloud-scale architecture that brings together big data, AI and compute farm, and storage tiers to work together as a single entity, while also being able to scale independently to address the IT issues in the modern data center.
Deep learning from scratch

Deep learning from scratch

February 15, 2020

You'll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you'll be prepared to dive deeper into the deep learning applications of your choice.
Deep learning on Apache Spark at CERNs Large Hadron Collider with Analytics Zoo

Deep learning on Apache Spark at CERNs Large Hadron Collider with Analytics Zoo

February 15, 2020

Sajan Govindan outlines CERNs research on deep learning in high energy physics experiments as an alternative to customized rule-based methods with an example of topology classification to improve real-time event selection at the Large Hadron Collider. CERN uses deep learning pipelines on Apache Spark using BigDL and Analytics Zoo open source software on Intel Xeon-based clusters.
Executive Briefing: Big data in the era of heavy worldwide privacy regulations

Executive Briefing: Big data in the era of heavy worldwide privacy regulations

February 14, 2020

California is following the EU's GDPR with the California Consumer Protection Act (CCPA) in 2020. Penalties for non-compliance, but many companies aren't prepared for this strict regulation. This session will explore the capabilities your data environment needs in order to simplify CCPA and GDPR compliance, as well as other regulations.
From isolated to connected: The metamorphosis of Revibe

From isolated to connected: The metamorphosis of Revibe

February 13, 2020

Its no surprise that Revibe needed to learn how to evolve to satiate the current data hungry market. It launched its first hardware-only device in 2015 and quickly learned that to stay alive, the company needed to get its hands into data. Gwen Campbell discusses Revibe's metamorphosis from a hardware company to a data company and shares lessons learned along the way.
Gaining new insight into online customer behavior using AI

Gaining new insight into online customer behavior using AI

February 13, 2020

Customer satisfaction is a key success factor for any business. Moise Convolbo highlights the process to capture relevant customer behavioral data, cluster the user journey by different patterns, and draw conclusions for data-informed business decisions.
Machine learning in data quality management

Machine learning in data quality management

February 12, 2020

Jennifer Yang discusses a use case that demonstrates how to use machine learning techniques in the data quality management space in the financial industry. You'll discover the results of applying various machine learning techniques in the four most commonly defined data validation categories and learn approaches to operationalize the machine learning data quality management solution.
Parquet modular encryption: Confidentiality and integrity of sensitive column data

Parquet modular encryption: Confidentiality and integrity of sensitive column data

February 11, 2020

The Apache Parquet community is working on a column encryption mechanism that protects sensitive data and enables access control for table columns. Many companies are involved, and the mechanism specification has recently been signed off on by the community management committee. Gidon Gershinsky explores the basics of Parquet encryption technology, its usage model, and a number of use cases.
Spark on Kubernetes for data science

Spark on Kubernetes for data science

February 10, 2020

Spark on Kubernetes is a winning combination for data science that stitches together a flexible platform harnessing the best of both worlds. Jordan Volz gives a brief overview of Spark and Kubernetes, the Spark on Kubernetes project, why its an ideal fit for data scientists who may have been dissatisfied with other iterations of Spark in the past, and some applications.
Chameleon: Expanding Open-Source Ambari for HPC

Chameleon: Expanding Open-Source Ambari for HPC

February 8, 2020

High performance computing (HPC) community is increasingly demanding big data processing beyond traditional simulation-based computation. Hadoop ecosystem has a roadmap that includes HPC support inclu …
Where is my Code Vulnerable: Matching CVEs and Source Code

Where is my Code Vulnerable: Matching CVEs and Source Code

February 7, 2020

Surprisingly, often it's hard to find the precise correspondence between a known software vulnerability (in CVE) and the exact origin of the software (Maven coordinates or github repo/tag). Ideally, t …
Democratizing fintech: Enabling financial services for all through open source banking

Democratizing fintech: Enabling financial services for all through open source banking

January 30, 2020

A convergence of trends and technologies is enabling the democratization of financial servicesbig data, AI, the cloud, smartphone ubiquity, national IDs, blockchain, and open banking. However, there's one missing factoropen source bankingthat will scale the movement and unlock financial services for all, from the unbanked in India to the underbanked in America.
Optimizing analytical queries on Cassandra by 100x

Optimizing analytical queries on Cassandra by 100x

January 26, 2020

Cassandra is one of the most popular datastores in big data and ML applications. Data analysis at scale with fast query response is critical for business needs, and while Cassandra with Spark integration allows running an analytical workload, it can be slow. Shradha Ambekar dives into the challenges faced at Intuit and the solutions her team implemented to improve performance by 100x.
Overview of Data Governance

Overview of Data Governance

January 26, 2020

Paco Nathan offers an overview of its history, themes, tools, process, standards, and morepartly based on interviewing experts in this field about issues and best practices. Join in to learn what impact machine learning has on data governance and vice versa, along with an overview of open source projects and open standards in this space.
Walking on Hexagons: Unification of Urban Data with H3

Walking on Hexagons: Unification of Urban Data with H3

January 24, 2020

We are proposing the use of H3, as a standard hexagonal discrete global grid system, to index data for analysis. When it comes to big data, H3 is uniquely suited to this analysis because of its hexago …
(Unifying analytics and AI on big data for faster insights at scale)

(Unifying analytics and AI on big data for faster insights at scale)

January 22, 2020

Ziya Ma walks you through Intels scalable data insights strategy and related big data analytics and AI technologies such as Analytics Zooan end-to-end analytics and AI pipeline for developing full solutions with Apache Spark on Intel Xeon and Intel Optane DC Persistent Memory at scale. She highlights customers use cases and collaboration with industry leaders throughout.
Big data analytics in the public cloud: Challenges and opportunities

Big data analytics in the public cloud: Challenges and opportunities

January 14, 2020

Jian Zhang, Chendi Xue, and Yuan Zhou explore the challenges of migrating big data analytics workloads to the public cloud (e.g., performance lost and missing features) and demonstrate how to use a new in-memory data accelerator leveraging persistent memory and RDMA NICs to resolve this issues and enable new opportunities for big data workloads on the cloud.
Building a serverless big data application on AWS

Building a serverless big data application on AWS

January 14, 2020

Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join in to learn how to incorporate serverless concepts into your big data architectures. You'll explore design patterns to ingest, store, and analyze your data as you build a big data application using AWS technologies such as S3, Athena, Kinesis, and more.
Dealing with data scarcity in natural language processing

Dealing with data scarcity in natural language processing

January 13, 2020

In this age of big data, NLP professionals are all too often faced with a lack of data: written language is abundant, but labeled text is much harder to come by. Yves Peirsman outlines the most effective ways of addressing this challenge, from the semiautomatic construction of labeled training data to transfer learning approaches that reduce the need for labeled training examples.
Evaluating cybersecurity defenses with a data science approach

Evaluating cybersecurity defenses with a data science approach

January 12, 2020

Cybersecurity analysts are under siege to keep pace with the ever-changing threat landscape. The analysts are overworked as they are bombarded with and burned out by the sheer number of alerts that they must carefully investigate. Brennan Lodge and Jay Kesavan explain how to use a data science model for alert evaluations to empower your cybersecurity analysts.
Executive Briefing: Big data in the era of heavy worldwide privacy regulations

Executive Briefing: Big data in the era of heavy worldwide privacy regulations

January 11, 2020

The implications of new privacy regulations for data management and analytics, such as the General Data Protection Regulation (GDPR) and the upcoming California Consumer Protection Act (CCPA), can seem complex. Mark Donsky and Nikki Rouda highlight aspects of the rules and outline the approaches that will assist with compliance.
Executive Briefing: Overview of data governance

Executive Briefing: Overview of data governance

January 11, 2020

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.