data science

Deep learning with TensorFlow and Spark using GPUs and Docker containers

Deep learning with TensorFlow and Spark using GPUs and Docker containers

January 12, 2020

Organizations need to keep ahead of their competition by using the latest AI, ML, and DL technologies such as Spark, TensorFlow, and H2O. The challenge is in how to deploy these tools and keep them running in a consistent manner while maximizing the use of scarce hardware resources, such as GPUs. Thomas Phelan discusses the effective deployment of such applications in a container environment.
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.
How do you evolve your data infrastructure?

How do you evolve your data infrastructure?

January 10, 2020

Developing data infrastructure is not trivial; neither is changing it. It takes effort and discipline to make changes that can affect your team. Neelesh Salian discusses how Stitch Fix's data platform team maintains and innovates its infrastructure for the company's data scientists.
Making data science useful

Making data science useful

January 8, 2020

Despite the rise of data engineering and data science functions in today's corporations, leaders report difficulty in extracting value from data. Many organizations arent aware that they have a blindspot with respect to their lack of data effectiveness, and hiring experts doesnt seem to help. Join Cassie Kozyrkov to talk about how you can change that.
Practicing data science: A collection of case studies

Practicing data science: A collection of case studies

January 7, 2020

Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
Serverless for data and AI

Serverless for data and AI

January 6, 2020

What is serverless, and how can it be utilized for data analysis and AI? Avner Braverman outlines the benefits and limitations of serverless with respect to data transformation (ETL), AI inference and training, and real-time streaming. This is a technical talk, so expect demos and code.
Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData)

Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData)

January 4, 2020

Nanda Vijaydev shares practical examples ofand lessons learned fromML/DL use cases in financial services, healthcare, and other industries. You'll learn how to quickly deploy containerized multinode environments for TensorFlow and other ML/DL tools in a multitenant architecture either on-premises, in the cloud, or in a hybrid environment.
Why is it so hard to do AI for good?

Why is it so hard to do AI for good?

January 4, 2020

DataKind UK has been working in data for good since 2013, helping over 100 UK charities to do data science for the benefit of their users. Some of those projects have delivered above and beyond expectations; others haven't. Duncan Ross and Giselle Cory explain how to identify the right data for good projects and how this can act as a framework for avoiding the same problems across industry.
Better Together Diversity Networking Lunch

Better Together Diversity Networking Lunch

January 2, 2020

If youd like to make new professional connections and hear ideas for supporting diversity in the tech community, come to the diversity and inclusion networking lunch on Wednesday.
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

January 2, 2020

Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting)

Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting)

January 1, 2020

Bill Roberts discusses artificial intelligence for strategic business insight and for the solution of new business problems using advanced cognitive algorithms. Along the way, he highlights the importance of using the right algorithm for a given business challenge, using real-world examples.
One-click deployment for containerized ML and DL environments

One-click deployment for containerized ML and DL environments

December 29, 2019

Nanda Vijaydev explains how to spin up instant ML/DL environments using containersall while ensuring enterprise-grade security and performance. Find out how to provide your data science teams with on-demand access to the tools and data they need, whether on-premises or in the cloud.
Turn devices into data scientistsat the edge

Turn devices into data scientistsat the edge

December 28, 2019

Todays approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it allleaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head.
Architecting Enterprise Platforms for CI/CD, Cloud and SRE with AI and Analytics

Architecting Enterprise Platforms for CI/CD, Cloud and SRE with AI and Analytics

December 26, 2019

Are machines ready to write, test, debug and patch enterprise software? What are the emerging capabilities? Is it a fantasy or fast happening already? How are enterprises strategizing and retooling?Gi …
Deploying data science for national economic statistics

Deploying data science for national economic statistics

December 25, 2019

Jeff Chen shares strategies for overcoming time series challenges at the intersection of macroeconomics and data science, drawing from machine learning research conducted at the Bureau of Economic Analysis aimed at improving its flagship product the gross domestic product.
Managing data science in the enterprise

Managing data science in the enterprise

December 22, 2019

The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders must deliver measurable impact on an increasing share of an enterprise's KPIs. Joshua Poduska, Kimberly Shenk, and Mac Steele explain how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage.
New directions in record linkage

New directions in record linkage

December 22, 2019

The US Census Bureau has been involved in record linkage projects for over 40 years. In that time, there's been a lot of change in computing capabilities and new techniques, and the Census Bureau is reviewing an inventory of linkage methodologies. Yves Thibaudeau describes the progress made so far in identifying specific record linkage techniques for specific applications.
Nutrition data science

Nutrition data science

December 22, 2019

Noah Gift and Michelle Davenport explore exciting ideas in nutrition using data science; specifically, they analyze the detrimental relationship between sugar and longevity, obesity, and chronic diseases.
Scoring your business in the AI matrix (sponsored by Dataiku)

Scoring your business in the AI matrix (sponsored by Dataiku)

December 21, 2019

One widely accepted definition of AI is that it means going beyond simple statistics to mimic human skills in perception, learning, interaction, and decision making. Jed Dougherty tightens up this definition by sharing examples on a matrix that breaks down the different parts of that definition and how they might manifest themselves in data science projects at different levels.
Serverless for data and AI

Serverless for data and AI

December 21, 2019

What is serverless, and how can it be utilized for data analysis and AI? Avner Braverman outlines the benefits and limitations of serverless with respect to data transformation (ETL), AI inference and training, and real-time streaming. This is a technical talk, so expect demos and code.
Strata Data Awards: Winners Announced

Strata Data Awards: Winners Announced

December 21, 2019

The Strata Data Awards recognize the most innovative startups, leaders, and data science projects from Strata sponsors and exhibitors around the world. Join us during keynotes for the announcement of the winners.
The magic behind your Lyft ride prices: A case study on machine learning and streaming

The magic behind your Lyft ride prices: A case study on machine learning and streaming

December 20, 2019

Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalabilityallowing the pricing system to be more adaptable to real-world changes.
Using the full spectrum of data science to drive business decisions

Using the full spectrum of data science to drive business decisions

December 19, 2019

Thanks to the rapid growth in data resources, business leaders now appreciate the importance (and the challenge) of mining information from data. Join in as a group of LinkedIn's data scientists share their experiences successfully leveraging emerging techniques to assist in intelligent decision making.
What the reproducibility problem means for your business

What the reproducibility problem means for your business

December 19, 2019

Academic research has been plagued by a reproducibility crisis in fields ranging from medicine to psychology. Stuart Buck explains how to take precautions in your data analysis and experiments so as to avoid those reproducibility problems.
Building a robust content recommendation platform for 60 million news readers

Building a robust content recommendation platform for 60 million news readers

December 18, 2019

Matt Chapman leads a walkthrough of the architecture and open source components that serve Tribune Publishing's content recommendation system, powered by online machine learning at scale. Find out how multiple publications, multiple recommendation algorithms, and one scalable architecture regularly achieve double the performance of the legacy solution.
KubeFlows Serverless Component: 10x Faster, a 1/10 of the Effort

KubeFlows Serverless Component: 10x Faster, a 1/10 of the Effort

December 12, 2019

Serverless simplifies data science by automating the process of code to container and enables users to add instrumentation and auto-scaling with minimum overhead. However, serverless has many limitati …
Realizing End to End Reproducible Machine Learning on Kubernetes

Realizing End to End Reproducible Machine Learning on Kubernetes

December 1, 2019

Industry adaptation of data-science has grown rapidly in the last few years. The probabilistic nature of this space requires the right tools and techniques to ensure that the answers produced are reli …
Tutorial: From Notebook to Kubeflow Pipelines: An End-to-End Data Science Workflow

Tutorial: From Notebook to Kubeflow Pipelines: An End-to-End Data Science Workflow

November 22, 2019

Please bring your laptop fully charged as we will have limited charging stations available in the room.This session targets data scientists and ML engineers who want to leverage Kubernetes to scale up …