Sep 23–26, 2019

Data Science, Machine Learning, & AI

Machine learning lets you discover hidden insight from your data. It's a simple idea with phenomenal impact and sophisticated use cases like recommenders, text mining, real-time analytics, large-scale anomaly detection, and business forecasting.

At Strata, you’ll get a deeper and broader understanding of machine and deep learning—take a look at the sessions below.

Featured Speakers

Monday-Tuesday, September 23-24: 2-Day Training (Platinum & Training passes)
Tuesday, September 24: Tutorials (Gold & Silver passes)
Wednesday, September 25: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes)
9:00 | Location: Auditorium
Strata Data Conference Keynotes
10:50
Morning break
Thursday, September 26: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes)
9:00 | Location: Auditorium
Strata Data Conference Keynotes
10:50
Morning break
Add to your personal schedule
9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 03
Secondary topics:  Deep Learning, Media and Advertising, Retail and e-commerce
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ Consulting)
In this two-days workshop, you will learn the different paradigms of recommendation systems and get introduced to the usage of deep-learning based approaches . By the end of the workshop, you will have enough practical hands-on knowledge to build, select, deploy and maintain a recommendation system for your problem. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 15/16
Secondary topics:  Deep dive into specific tools, platforms, or frameworks
Michael Cullan (The Data Incubator)
Michael Cullan walks you through developing a machine learning pipeline, from prototyping to production. You'll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 18
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but learners face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by elucidating the abstractions common to these systems. Through hands-on exercises, you'll overcome obstacles to getting started using new tools. Read more.
Add to your personal schedule
9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1E 07
Secondary topics:  Deep dive into specific tools, platforms, or frameworks, Deep Learning
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil offers an overview of TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications. Read more.
Add to your personal schedule
9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 12/14
Secondary topics:  Culture and Organization, Model Development, Governance, Operations
Sourav Dey (Manifold), Alex Ng (Manifold)
Many teams are still run as if data science is about experimentation, but those days are over. Now it must offer turnkey solutions to take models into production. We'll explain how to streamline a ML project and help your engineers work as an integrated part of production teams, using a Lean AI process and the Orbyter package for Docker-first data science. Read more.
Add to your personal schedule
9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 21/22
Secondary topics:  Model Development, Governance, Operations
Jules Damji (Databricks)
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work Read more.
Add to your personal schedule
9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 23/24
Secondary topics:  Text and Language processing and analysis
Alice Zhao (Metis)
As a data scientist, we are known to crunch numbers, but what happens when we run into text data? In this tutorial, I will walk through the steps to turn text data into a format that a machine can understand, share some of the most popular text analytics techniques, and showcase several natural language processing (NLP) libraries in Python including NLTK, TextBlob, spaCy and gensim. Read more.
Add to your personal schedule
9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 08
Secondary topics:  Deep Learning
Bruno Goncalves (Data For Science, Inc)
Students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying Deep Learning and Neural Networks. The code structure of the implementations provided is meant to closely resemble he way Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice. Read more.
Add to your personal schedule
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 12/14
Secondary topics:  Deep Learning, Financial Services, Text and Language processing and analysis
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
Add to your personal schedule
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 21/22
Secondary topics:  Cloud Platforms and SaaS, Deep dive into specific tools, platforms, or frameworks
Karthik Sonti (Amazon Web Services), Emily Webber (Amazon Web Services), Varun Rao Bhamidimarri (Amazon Web Services)
In this workshop we’ll introduce the Amazon SageMaker machine learning platform, followed by a high level discussion of recommender systems. Next we’ll dig into different machine learning approaches for recommender systems. Read more.
Add to your personal schedule
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 23/24
Secondary topics:  Deep dive into specific tools, platforms, or frameworks, Text and Language processing and analysis
David Talby (Pacific AI), Alex Thomas (Indeed), Saif Addin Ellafi (John Snow Labs)
This is a hands-on tutorial on state-of-the-art NLP using the highly performant, highly scalable open-source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve. Read more.
Add to your personal schedule
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1E 08
Secondary topics:  Streaming and IoT, Temporal data and time-series analytics
Sophie Watson (Red Hat), William Benton (Red Hat)
In this hands-on workshop, we’ll introduce several data structures that let you answer interesting queries about massive data sets in fixed amounts of space and constant time. This seems like magic, but we'll explain the key trick that makes it possible and show you how to use these structures for real-world machine learning and data engineering applications. Read more.
Add to your personal schedule
11:20am12:00pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Ethics
Harsha Nori (Microsoft), Sameul Jenkins (Microsoft), Rich Caruana (Microsoft)
Understanding decisions made by machine learning systems is critical for sensitive uses, ensuring fairness, and debugging production models. Interpretability is a maturing field of research that presents many options for trying to understand model decisions. Microsoft is releasing new tools to help you train powerful, interpretable models and interpret decisions of existing blackbox systems. Read more.
Add to your personal schedule
11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Financial Services, Temporal data and time-series analytics
Ying Yau (AllianceBernstein)
Time series forecasting techniques can be applied in a wide range of scientific disciplines, business scenarios, and policy settings. This session discusses the application of deep learning techniques to time series forecasting and compares them to time series statistical models when forecasting time series with trends, multiple seasonality, regime switch, and exogenous series. Read more.
Add to your personal schedule
11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Culture and Organization
Ann Spencer (Domino Data Lab), Paco Nathan (Derwen, Inc.), Amy Heineike (Primer), Pete Warden (TensorFlow)
Are you a data scientist that has wondered "why does it take so long to deploy my model into production?" Are you an engineer that has ever thought "data scientists have no idea what they want"? You are not alone. Join us for a lively discussion panel, with industry veterans, to chat about best practices and insights regarding how to increase collaboration when developing and deploying models. Read more.
Add to your personal schedule
11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 12/14
Ted Dunning (MapR)
Feature engineering is generally the section that gets left out of machine learning books, but it is also the most critical part in practice. I will provide a variety of techniques, a few well known, but some rarely spoken of outside the tribal lore of top teams, including how to handle categorical inputs, natural language, transactions and more all in the context of modern machine learning. Read more.
Add to your personal schedule
1:15pm1:55pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Health and Medicine, Text and Language processing and analysis
Saif Addin Ellafi (John Snow Labs), Scott Hoch (Deep6.ai)
Recruiting patients for clinical trials is a major challenge in drug development. This talk explains how Deep6 utilizes Spark NLP to scale its training and inference pipelines to millions of patients while achieving state-of-the-art accuracy. It covers the technical challenges, the architecture of the full solution, and lessons learned. Read more.
Add to your personal schedule
1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Financial Services, Health and Medicine
Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images. Read more.
Add to your personal schedule
1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Data, Analytics, and AI Architecture, Financial Services, Retail and e-commerce
James Tang (WalmartLabs), Yiyi Zeng (WalmartLabs), Linhong Kang (WalmartLabs)
How No1 retailer provides secure and seamless shopping experience through machine learning and large scale data analysis on centralized platform. Read more.
Add to your personal schedule
1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Deep Learning
Shioulin Sam (Cloudera Fast Forward Labs)
Supervised machine learning requires large labeled datasets - a prohibitive limitation in many real world applications. What if machines could learn with few labeled examples? This talk explores and demonstrates an algorithmic solution that relies on collaboration between human and machines to label smartly, and discuss product possibilities. Read more.
Add to your personal schedule
2:05pm2:45pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Text and Language processing and analysis
Panos Alexopoulos (Textkernel BV)
In an era where discussions among data scientists are monopolized by the latest trends in Machine Learning, the role of Semantics in Data Science is often underplayed. In this talk, I present real-world cases where making fine, seemingly pedantic, distinctions in the meaning of data science tasks and their related data, has helped improve significantly their effectiveness and value. Read more.
Add to your personal schedule
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Temporal data and time-series analytics, Transportation and Logistics
Keshav Peswani (Expedia Group), Ashish Aggarwal (Expedia Group)
Observability is the key in modern architecture to quickly detect and repair problems in microservices. Modern observability platforms have evolved beyond simple application logs and now include distributed tracing systems like Zipkin, Haystack. Combining them with real time intelligent alerting mechanisms with accurate alerts helps in automated detection of these problems. Read more.
Add to your personal schedule
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Deep dive into specific tools, platforms, or frameworks, Transportation and Logistics
Nan Zhu (Uber), Felix Cheung (Uber)
XGBoost has been widely deployed in companies across the industry. This talk begins with introducing the internals of distributed training in XGBoost and then demonstrate how XGBoost resolves the business problem in Uber with a scale to thousands of workers and 10s of TB training data. Read more.
Add to your personal schedule
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Ethics, Privacy and Security, Retail and e-commerce
Mikio Braun (Zalando SE)
With ML becoming more and more mainstream, the side effects of using machine learning and AI on our lives become more and more visible. One has to take extra measures to make machine learning models fair and unbiased In addition, awareness for preserving the privacy in ML models is rapidly growing. Read more.
Add to your personal schedule
2:55pm3:35pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Text and Language processing and analysis
Gerard de Melo (Rutgers University)
What kinds of sentiment and emotions do consumers associate with a text? With new data-driven approaches, organizations can better pay attention to what is being said about them in different markets. We can also consider the fonts and color palettes best-suited to convey specific emotions, so that organizations can make informed choices when presenting information to consumers. Read more.
Add to your personal schedule
2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Temporal data and time-series analytics
Tony Xing (Microsoft), Bixiong Xu (Microsoft), Congrui Huang (Microsoft), Qun Ying (Microsoft)
Anomaly Detection may sound old fashioned yet super important in many industry applications. How about doing this in a computer vision way? Come to our talk to learn a novel Anomaly Detection algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN), and how this novel method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
Add to your personal schedule
2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Media and Advertising, Retail and e-commerce
Fei Wang (CarGurus), Michael Brautbar (CarGurus)
This session will present the case study for the CarGurus TV Attribution Model. Attendees will learn how the creation of a causal inference model can be leveraged to calculate cost per acquisition (CPA) of TV spend and measure effectiveness when compared to CPA of Digital Performance Marketing spend. Read more.
Add to your personal schedule
2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Financial Services
Jari Koister (FICO )
Machine Learning and Constraint-based Optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. This talk describes how they are similar, how they differ and how they can be used to solve complex problems with amazing results. Read more.
Add to your personal schedule
4:35pm5:15pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Text and Language processing and analysis
John Berryman (Eventbrite)
Eventbrite is exploring a new machine learning approach that allows us to harvest data from customer search logs and automatically tag events based upon their content. The results have allowed us to provide users with a better inventory browsing experience. Read more.
Add to your personal schedule
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Data Integration and Data Processing, Deep Learning, Financial Services
Siddha Ganju (Nvidia), Meher Kasam (Square)
Optimizing deep neural nets to run efficiently on mobile devices. Read more.
Add to your personal schedule
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Retail and e-commerce, Temporal data and time-series analytics
Robert Pesch (inovex GmbH), Robin Senge (inovex GmbH)
In this talk, we outline the development process, the statistical modeling, the data-driven decision making, and the components needed for productionizing a fully automated and highly scalable demand forecasting system for an online grocery shop for a billion-dollar retail group in Europe. Read more.
Add to your personal schedule
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Media and Advertising, Temporal data and time-series analytics
Criteo’s infrastructure provides capacity and connectivity to host Criteo’s platform and applications. The evolution of our infrastructure is driven by the ability to forecast Criteo’s traffic demand. In this talk, we explain how Criteo uses Bayesian Dynamic time series models to accurately forecast its traffic load and optimize hardware resources across data centers. Read more.
Add to your personal schedule
5:25pm6:05pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Culture and Organization, Text and Language processing and analysis
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Emily Webber (Amazon Web Services)
Mansplaining. Know it? Hate it? Want to make it go away? In this session we tackle the chronic problem of men talking over or down to women and its negative impact on career progression for women. We will also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds. We discuss ownership of the problem for both women and men, and suggest helpful strategies. Read more.
Add to your personal schedule
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Model Development, Governance, Operations
The common perception of deep learning is that it results in a fully self-contained model. However, in most cases these models have similar requirements for data pre-processing as more "traditional" machine learning. Despite this, there are few standard solutions for deploying end-to-end deep learning. In this talk, I show how the ONNX format and ecosystem is addressing this challenge. Read more.
Add to your personal schedule
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 08/10
Secondary topics:  Media and Advertising
Aaron Owen (Major League Baseball), Matt Horton (Major League Baseball), Josh Hamilton (MLB)
Utilizing SAS, Python, and AWS Sagemaker, MLB’s data science team discusses how it predicts ticket purchasers’ likelihoods to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service. Read more.
Add to your personal schedule
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Retail and e-commerce
Subhasish Misra (Walmart )
Causal questions are ubiquitous. Randomized tests are considered to be the gold standard for these. However, such tests are not always feasible and then, one just has observational data to get to causal insights. Techniques such as matching offer a solve then. This talk will offer a take on the above aspects, plus share practical tips when trying to infer causal effects. Read more.
Add to your personal schedule
11:20am12:00pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Secondary topics:  Culture and Organization, Retail and e-commerce, Transportation and Logistics
Brian Keng (Rubikloud Technologies Inc)
Automating decisions require a system to consider more than just a data-driven prediction. Real-world decisions require additional constraints and fuzzy objectives to ensure that they are robust and consistent with business goals. This talk will describe how to leverage modern machine learning methods and traditional mathematical optimization techniques for decision automation. Read more.
Add to your personal schedule
11:20am12:00pm Thursday, September 26, 2019
Location: 1A 06/07
Shital Shah (Microsoft Research)
How do we visualize what exactly deep learning is doing? Taming the massive models, data and training times requires new way of thinking about them. In talk we will introduce explore new tools and methods to understand AI better. Explaining the decisions made by AI not only helps us accelerate its development but also make it safe and more trustworthy. Read more.
Add to your personal schedule
11:20am12:00pm Thursday, September 26, 2019
Location: 1A 08/10
Secondary topics:  Financial Services, Temporal data and time-series analytics
Anjali Samani (CircleUp)
The application of smoothing and imputation strategies is common practice in predictive modelling and time series analysis. With a technique-agnostic approach, this session will provide qualitative and quantitative frameworks that address questions related to smoothing and imputation of missing values to improve data density. Read more.
Add to your personal schedule
11:20am12:00pm Thursday, September 26, 2019
Location: 1A 12/14
Secondary topics:  Ethics
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We'll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our machine learning models. Read more.
Add to your personal schedule
1:15pm1:55pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Secondary topics:  Deep dive into specific tools, platforms, or frameworks, Deep Learning
Victor Dibia (Cloudera Fast Forward Labs)
Recent advances in Machine Learning frameworks for the browser such as Tensorflow.js provides opportunity to craft truly novel experiences within front-end applications. This talk explores the state of the art for Machine Learning in the browser using Tensorflow.js and covers its use in the design of Handtrack.js - a library for prototyping real time hand detection in the browser. Read more.
Add to your personal schedule
1:15pm1:55pm Thursday, September 26, 2019
Location: 1A 08/10
Secondary topics:  Temporal data and time-series analytics
Alfred Whitehead (Klick), Clare Jeon (KLICK INC)
What will tomorrow’s temperature be? My blood glucose levels tonight before bed? Time series forecasts depend on sensors or measurements made out in the real, messy world. Those sensors flake out, get turned off, disconnect, and otherwise conspire to cause missing data in our signals. We will show a number of methods for handling data gaps and give advice on which to consider and when. Read more.
Add to your personal schedule
1:15pm1:55pm Thursday, September 26, 2019
Location: 1A 12/14
Secondary topics:  Text and Language processing and analysis
Sandra Carrico (Glynt.ai)
This talk motivates mixed formal learning, explains it and outlines one machine learning example that previously used large numbers of examples and now learns with either zero or a handful of training examples. It maps apparently idiosyncratic techniques to Mixed Formal Learning, a general AI architecture that you can use in your projects. Read more.
Add to your personal schedule
2:05pm2:45pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Secondary topics:  Streaming and IoT, Telecom, Temporal data and time-series analytics
Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Télécom ParisTech)
In this talk, we show how to develop a machine learning pipeline for streaming data using the StreamDM framework (https://github.com/huawei-noah/streamDM). We also introduce how to use StreamDM for supervised and unsupervised learning tasks, show examples of online preprocessing methods, and how to expand the framework adding new learning algorithms or preprocessing methods. Read more.
Add to your personal schedule
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Streaming and IoT
Ryan Foltz (Exabeam)
Unmanaged & foreign devices in the corporate networks pose a security risk. The 1st step toward reducing risk from these devices is the ability to identify them. To have a comprehensive device management program, we proposed a machine learning model based on Deep Learning to perform anomaly detection based on only device names to flag devices that do not follow device naming structures. Read more.
Add to your personal schedule
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 08/10
Secondary topics:  Temporal data and time-series analytics
Anais Jackie Dotis (InfluxData)
Did you know that Classical algorithms outperform Machine Learning methods in time series forecasting? I’ll show you how I used the Holt-Winters forecasting algorithm to predict water levels in a creek. Read more.
Add to your personal schedule
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12/14
Secondary topics:  Data quality, data governance and data lineage, Media and Advertising, Model Development, Governance, Operations
Andrew Leamon (Comcast), Wadkar Sameer (Comcast NBCUniversal)
And overview of the Data Management and privacy challenges around automating ML model (re)deployments and stream based inferencing at scale. Read more.
Add to your personal schedule
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning
Sajan Govindan (Intel), Luca Canali (CERN)
We will show CERN’s research on applying 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 experiments. CERN implemented deep learning pipelines on Apache Spark using BigDL and Analytics Zoo open source software on Intel Xeon-based clusters Read more.
Add to your personal schedule
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 12/14
David Mack (Octavian)
Graphs are a powerful way to represent knowledge. Organizations (in fields such as bio-sciences and finance) are starting to amass large knowledge graphs, but lack the machine-learning tools to extract the insights they need from them. In this presentation, I’ll give an overview of what insights are possible and survey the most popular approaches. Read more.
Add to your personal schedule
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 08/10
Secondary topics:  Deep dive into specific tools, platforms, or frameworks
Chad Scherrer (Metis)
This talk will explore the basic ideas in Soss, a new probabilistic programming library for Julia. Soss allows a high-level representation of the kinds of models often written in PyMC3 or Stan, and offers a way to programmatically specify and apply model transformations like approximations or reparameterizations. Read more.
Add to your personal schedule
4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning
Naoto Umemori (NTT DATA Corporation), Masaru Dobashi (NTT Data Corp.)
Giant Hogweed is a highly toxic plant. Our project aims to automate the process of detecting the Giant Hogweed by exploiting technologies like drones and image recognition/detection using Machine Learning. We show you how we designed the architecture, how we took advantage of both of Big Data and Machine / Deep Learning technologies (e.g. Hadoop, Spark and TensorFlow) and lessons learned. Read more.
Add to your personal schedule
4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 12/14
Secondary topics:  Transportation and Logistics
Brandy Freitas (Pitney Bowes)
In this session, Brandy Freitas from Pitney Bowes will cover the interplay between graph analytics and machine learning, improved feature engineering with graph native algorithms, and harnessing the power of graph structure for machine learning through node embedding. Read more.
Add to your personal schedule
4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 08/10
Secondary topics:  Temporal data and time-series analytics
Jeroen Janssens (Data Science Workshops B.V.)
In this talk, we present Stochastic Outlier Section (SOS), an unsupervised algorithm for detecting anomalies in large, high-dimensional data. SOS has been implemented in Python, R, and most recently, Spark. First, we illustrate the idea and intuition behind SOS. Subsequently, we demonstrate our implementation of SOS on top of Spark. Finally, we apply SOS to a real-world use case. Read more.

Contact us

confreg@oreilly.com

For conference registration information and customer service

partners@oreilly.com

For more information on community discounts and trade opportunities with O’Reilly conferences

strataconf@oreilly.com

For information on exhibiting or sponsoring a conference

Contact list

View a complete list of Strata Data Conference contacts