Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Data Science & Machine Learning
March 5-8, 2018
San Jose, CA
If you're in data, you need to understand machine learning
Using algorithms that learn iteratively, 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.
Strata is a unique opportunity to get unparalleled depth and breadth in machine and deep learning—take a look at the sessions below.
| Monday-Tuesday March 5-6: 2-Day Training (Platinum & Training passes) |
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| Tuesday March 6: Tutorials (Gold & Silver passes) |
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| Wednesday March 7: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 8:45 | Location: Salon 1&2 Strata Data Conference Keynotes |
| 10:30am Morning break |
| Thursday March 8: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
|---|
| 8:45 | Location: Salon 1&2 Strata Data Conference Keynotes |
| 10:30am Morning break |
9:00am - 5:00pm Monday, March 5 & Tuesday, March 6
Location: 212 A-B
Brooke Wenig (Databricks)
Brooke Wenig walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs.
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9:00am - 5:00pm Monday, March 5 & Tuesday, March 6
Location: 212 C
Average rating:
(5.00, 1 rating)
Brian Bloechle demonstrates how to implement typical data science workflows using Apache Spark. You'll learn how to wrangle and explore data using Spark SQL DataFrames and how to build, evaluate, and tune machine learning models using Spark MLlib.
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9:00am - 5:00pm Monday, March 5 & Tuesday, March 6
Location: 111
Average rating:
(5.00, 1 rating)
PyTorch is a recent deep learning framework from Facebook that is gaining massive momentum in the deep learning community. Its fundamentally flexible design makes building and debugging models straightforward, simple, and fun. Delip Rao and Brian McMahan walk you through PyTorch's capabilities and demonstrate how to use PyTorch to build deep learning models and apply them to real-world problems.
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9:00am - 5:00pm Monday, March 5 & Tuesday, March 6
Location: San Jose Ballroom (salon 1&2), Marriott
Robert Schroll (The Data Incubator)
The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs, making it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll demonstrates TensorFlow's capabilities and walks you through building machine learning models on real-world data.
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9:00am - 5:00pm Monday, March 5 & Tuesday, March 6
Location: Willow Glen (1&2), Marriott
Zachary Glassman (The Data Incubator)
Zachary Glassman demonstrates how to build intelligent business applications using machine learning, taking you through each step in developing a machine learning pipeline, from prototyping to production. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend your knowledge by building two applications from real-world datasets.
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9:00am–5:00pm Tuesday, March 6, 2018
Location: LL20 D
Joseph Kambourakis (databricks)
Join Joseph Kambourakis for an introduction to Apache Spark 2.0 core concepts with a focus on Spark's machine learning library, using text mining on real-world data as the primary end-to-end use case.
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9:00am–12:30pm Tuesday, March 6, 2018
Location: LL21 A
Secondary topics:
Graphs and Time-seriesAverage rating:
(2.50, 4 ratings)
Since its arrival in early 2017, PyTorch has won over many deep learning researchers and developers due to its dynamic computation framework. Mo Patel and Neejole Patel walk you through using PyTorch to build a content recommendation model.
Read more.
9:00am–12:30pm Tuesday, March 6, 2018
Location: LL21 C/D
Mario Inchiosa (Microsoft),
Vanja Paunic (Microsoft),
Robert Horton (Microsoft),
Debraj GuhaThakurta (Microsoft),
Ali-Kazim Zaidi (Microsoft),
Tomas Singliar (Microsoft),
John-Mark Agosta (Microsoft)
Average rating:
(4.00, 4 ratings)
R and Python top the list of languages used in data science and machine learning, and data scientists and engineers fluent in one of these languages are increasingly marketable. Come learn how to build and operationalize machine learning models using distributed functions and do scalable, end-to-end data science in R and Python on single machines, Spark clusters, and cloud-based infrastructure.
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9:00am–12:30pm Tuesday, March 6, 2018
Location: LL21 E/F
Martin Görner (Google)
Average rating:
(5.00, 3 ratings)
Martin Görner walks you through training and deploying a machine learning system using popular open source library TensorFlow. Martin takes you from a conceptual overview all the way to building complex classifiers and explains how you can apply deep learning to complex problems in science and industry.
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9:00am–12:30pm Tuesday, March 6, 2018
Location: 210 A/E
Average rating:
(4.44, 9 ratings)
Burcu Baran, Wei Di, Michael Li, and Chi-Yi Kuan walk you through the big data analytics and data science lifecycle and share their experience and lessons learned leveraging advanced analytics and machine learning techniques such as predictive modeling to drive and grow business at LinkedIn.
Read more.
9:00am–5:00pm Tuesday, March 6, 2018
Location: LL20 A
Madhav Madaboosi (BP),
Meenakshisundaram Thandavarayan (Infosys),
Matt Conners (Microsoft),
Katie Malone (Civis Analytics),
Mike Prorock (mesur.io),
Thomas Miller (Northwestern University),
Ann Nguyen (Whole Whale),
Jennie Shin (Kaiser Permanente),
Valentin Bercovici (PencilDATA),
Wayde Fleener (General Mills),
Joe Dumoulin (Next IT),
Jules Malin (GoPro),
Taylor Martin Martin (O'Reilly Media),
Divya Ramachandran (Captricity)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.
Read more.
9:00am–5:00pm Tuesday, March 6, 2018
Location: LL20 B
David Boyle (Audience Strategies),
Violeta Hennessey (Warner Bros.),
April Chen (Civis Analytics),
Sridhar Alla (BlueWhale),
Noah Gift (UC Davis),
Blake Irvine (Netflix),
Kevin Lyons (Nielsen Marketing Cloud),
Jennifer Webb (SuprFanz),
Rizwan Patel (Caesars Entertainment),
Anthony Accardo (Disney),
Amanda Gerdes (Blizzard Entertainment),
Violeta Hennessey (Warner Bros.),
Aneesh Karve (Quilt),
David Boyle (Audience Strategies),
Pete Skomoroch (Workday)
Hear from innovators in ad tech, measurement, automation, and audience engagement about where the media industry is today—and where it's likely to go next.
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1:30pm–5:00pm Tuesday, March 6, 2018
Location: LL20 C
Average rating:
(5.00, 1 rating)
Natural language processing is a key component in many data science systems. David Talby, Claudiu Branzan, and Alex Thomas lead a hands-on tutorial on scalable NLP, using spaCy for building annotation pipelines, Spark NLP for building distributed natural language machine-learned pipelines, and Spark ML and TensorFlow for using deep learning to build and apply word embeddings.
Read more.
1:30pm–5:00pm Tuesday, March 6, 2018
Location: LL21 C/D
Average rating:
(4.00, 3 ratings)
Controlled experiments such as A/B tests have revolutionized the way software is being developed, allowing real users to objectively evaluate new ideas. Ronny Kohavi, Alex Deng, Somit Gupta, and Paul Raff lead an introduction to A/B testing and share lessons learned from one of the largest A/B testing platforms on the planet, running at Microsoft, which executes over 10K experiments a year.
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1:30pm–5:00pm Tuesday, March 6, 2018
Location: LL21 E/F
Average rating:
(4.00, 3 ratings)
Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow. You'll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client.
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1:30pm–5:00pm Tuesday, March 6, 2018
Location: 210 D/H
Average rating:
(4.50, 2 ratings)
Python lets you solve data science problems by stitching together packages from its ecosystem, but it can be difficult to choose packages that work well together. James Bednar and Philipp Rudiger walk you through a concise, fast, easily customizable, and fully reproducible recipe for interactive visualization of millions or billions of datapoints—all in just 30 lines of Python code.
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9:30am–9:40am Wednesday, March 7, 2018
Location: Grand Ballroom 220
Ben Lorica (O'Reilly)
Average rating:
(4.00, 8 ratings)
Ben Lorica shares emerging security best practices for business intelligence, machine learning, and mobile computing products and explores new tools, methods, and products that can help ease the way for companies interested in deploying secure and privacy-preserving analytics.
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11:00am–11:40am Wednesday, March 7, 2018
Location: LL20 A
Mike Lee Williams (Cloudera Fast Forward Labs)
Average rating:
(4.86, 7 ratings)
Interpretable models result in more accurate, safer, and more profitable machine learning products. But interpretability can be hard to ensure. Michael Lee Williams explores the growing business case for interpretability and its concrete applications, including churn, finance, and healthcare. Along the way, Michael offers an overview of the open source, model-agnostic tool LIME.
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11:00am–11:40am Wednesday, March 7, 2018
Location: LL20 C
Daniel Lurie (1989)
All successful startups thrive on tight product-market fit, which can produce homogenous initial user bases. To become the next big thing, your user base will need to diversify, and your product must change to accommodate new needs. Daniel Lurie explains how Pinterest leverages external data to measure racial and income diversity in its user base and changed user modeling to drive growth.
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11:00am–11:40am Wednesday, March 7, 2018
Location: LL20 D
Average rating:
(3.50, 2 ratings)
Artificial intelligence (AI) has tremendous potential to extend our capabilities and empower organizations to accelerate their digital transformation. Wee Hyong Tok and Danielle Dean demystify AI for big data professionals and explain how they can leverage and evolve their valuable big data skills by getting started with AI.
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11:00am–11:40am Wednesday, March 7, 2018
Location: LL21 B
Rajat Monga (Google)
Average rating:
(4.40, 5 ratings)
Rajat Monga offers an overview of TensorFlow's progress and adoption in 2017 before looking ahead to the areas of importance in the future—performance, usability, and ubiquity—and the efforts TensorFlow is making in those areas.
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11:00am–11:40am Wednesday, March 7, 2018
Location: Expo Hall 1
Secondary topics:
Expo Hall
Siddha Ganju (NVIDIA)
Siddha Ganju explains how the FDL lab at NASA uses artificial intelligence to improve and automate the identification of meteors above human-level performance using meteor shower images and recover known meteor shower streams and characterize previously unknown meteor showers using orbital data—research aimed at providing more warning time for long-period comet impacts.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: LL20 A
Dan Crankshaw (UC Berkeley RISELab)
Average rating:
(4.25, 4 ratings)
Clipper is an open source, general-purpose model-serving system that provides low-latency predictions under heavy serving workloads for interactive applications. Dan Crankshaw offers an overview of the Clipper serving system and explains how to use it to serve Apache Spark and TensorFlow models on Kubernetes.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: LL20 C
Miryung Kim (UCLA),
Muhammad Gulzar (UCLA)
Average rating:
(3.50, 2 ratings)
Even though we know that there are more data scientists in the workforce today, neither what those data scientists actually do nor what we even mean by data scientists has been studied quantitatively. Miryung Kim and Muhammad Gulzar share the results of a large-scale survey with 793 professional data scientists and detail several trends about data scientists in the software engineering context.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: LL20 D
Andrew Mattarella-Micke (Intuit)
Average rating:
(5.00, 1 rating)
When building a chatbot, it’s important to develop one that is humanized, has contextual responses, and can simulate true empathy for the end users. Andrew Mattarella-Micke shares how Intuit's data science team preps, cleans, organizes, and augments training data along with best practices he's learned along the way.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: LL21 B
Average rating:
(3.00, 1 rating)
The BigDL framework scales deep learning for large datasets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. Shivaram Venkataraman and Sergey Ermolin outline a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception and VGG.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: 210 D/H
Ari Gesher (Kairos Aerospace)
A warming planet needs precise, localized predictions about the effects of climate change to make good long-term and medium-term economic decision making. Ari Gesher demonstrates how to use a mix of physical simulation, enhanced scientific models, machine learning verification, and high-scale computing to predict and package climate predictions as data products.
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11:50am–12:30pm Wednesday, March 7, 2018
Location: Expo Hall 1
Secondary topics:
Expo HallAverage rating:
(3.50, 2 ratings)
David Talby and Santosh Kulkarni explain how Kaiser Permanente uses the open source NLP library for Apache Spark to tackle one of the most common challenges with applying natural language process in practice: integrating domain-specific NLP as part of a scalable, performant, measurable, and reproducible machine learning pipeline.
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1:50pm–2:30pm Wednesday, March 7, 2018
Location: LL20 A
Secondary topics:
Graphs and Time-series
Alexandra Gunderson (Arundo Analytics)
Average rating:
(5.00, 1 rating)
Heavy industries, such as oil and gas, have tremendous amounts of data from which predictive models could be built, but it takes weeks or even months to create a comprehensive dataset from all of the various data sources. Alexandra Gunderson details the methodology behind an industry-tested approach that incorporates machine learning to structure and link data from different sources.
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1:50pm–2:30pm Wednesday, March 7, 2018
Location: LL20 C
Secondary topics:
Graphs and Time-series
Andrew Ray (Sam’s Club Technology)
Average rating:
(3.00, 3 ratings)
Andrew Ray offers a brief introduction to the distributed graph algorithm abstractions provided by Pregel, PowerGraph, and GraphX, drawing on real-world examples, and provides historical context for the evolution between these three abstractions.
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1:50pm–2:30pm Wednesday, March 7, 2018
Location: LL20 D
Average rating:
(5.00, 1 rating)
Intel has been deeply involved in Spark from its earliest moments. Vincent Xie and Peng Meng share what Intel has been working on with Spark ML and introduce the methodology behind Intel's work on SparkML optimization.
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1:50pm–2:30pm Wednesday, March 7, 2018
Location: LL21 B
Secondary topics:
Graphs and Time-series
Kyle Grove (Teradata)
Average rating:
(5.00, 5 ratings)
Kyle Grove explains how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organizations have built models with increased accuracy to drive more profitable lending decisions while being explainable to regulators.
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1:50pm–2:30pm Wednesday, March 7, 2018
Location: Expo Hall 1
Secondary topics:
Expo HallAverage rating:
(4.00, 3 ratings)
Deploying machine learning models and deep learning models in production is hard. Harish Doddi and Jerry Xu outline the enterprise data science lifecycle, covering how production model deployment flow works, challenges, best practices, and lessons learned. Along the way, they explain why monitoring models in the production should be mandatory.
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2:40pm–3:20pm Wednesday, March 7, 2018
Location: LL20 A
Secondary topics:
Graphs and Time-series
Baron Schwartz (VividCortex)
Average rating:
(4.80, 5 ratings)
Anomaly detection is white hot in the monitoring industry, but many don't really understand or care about it, while others repeat the same pattern many times. Why? And what can we do about it? Baron Schwartz explains how he arrived at a "post-anomaly detection" point of view.
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2:40pm–3:20pm Wednesday, March 7, 2018
Location: LL20 C
Evan Sparks (Determined AI)
Average rating:
(5.00, 1 rating)
Deep learning has shown tremendous improvements in a number of areas and has justifiably generated enormous excitement. However, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding widespread enterprise adoption. Evan Sparks details fundamental challenges facing organizations looking to adopt deep learning and shares possible solutions.
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2:40pm–3:20pm Wednesday, March 7, 2018
Location: LL20 D
Joseph Bradley (Databricks)
Average rating:
(5.00, 1 rating)
Joseph Bradley discusses common paths to productionizing Apache Spark MLlib models and shares engineering challenges and corresponding best practices. Along the way, Joseph covers several deployment scenarios, including batch scoring, Structured Streaming, and real-time low-latency serving.
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2:40pm–3:20pm Wednesday, March 7, 2018
Location: LL21 B
Average rating:
(3.50, 2 ratings)
Mohamed AbdelHady and Zoran Dzunic demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained on a Spark cluster using millions of PubMed abstracts and then used as features to train a LSTM recurrent neural network for entity extraction.
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4:20pm–5:00pm Wednesday, March 7, 2018
Location: LL20 A
Secondary topics:
Graphs and Time-series
Joseph Richards (GE Digital)
Average rating:
(5.00, 1 rating)
Deploying ML software applications for use cases in the industrial internet presents a unique set of challenges. Data-driven problems require approaches that are highly accurate, robust, fast, scalable, and fault tolerant. Joseph Richards shares GE's approach to building production-grade ML applications and explores work across GE in industries such as power, aviation, and oil and gas.
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4:20pm–5:00pm Wednesday, March 7, 2018
Location: LL20 C
Secondary topics:
Graphs and Time-seriesAverage rating:
(4.00, 1 rating)
Vlad Ionescu and Fabian Yamaguchi outline Code Property Graph (CPG), a unique approach that allows the functional elements of code to be represented in an interconnected graph of data and control flows, which enables semantic information about code to be stored scalably on distributed graph databases over the web while allowing them to be rapidly accessed.
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4:20pm–5:00pm Wednesday, March 7, 2018
Location: LL20 D
Rachita Chandra (IBM Watson Health)
Average rating:
(3.00, 1 rating)
Rachita Chandra outlines challenges and considerations for transforming a research prototype built for a single machine to a deployable healthcare solution that leverages Spark in a distributed environment.
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4:20pm–5:00pm Wednesday, March 7, 2018
Location: LL21 B
Secondary topics:
Graphs and Time-seriesAverage rating:
(4.60, 5 ratings)
Time series forecasting and anomaly detection is of utmost importance at Uber. However, the scale of the problem, the need for speed, and the importance of accuracy make anomaly detection a challenging data science problem. Andrea Pasqua and Anny Chen explain how the use of recurrent neural networks is allowing Uber to meet this challenge.
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5:10pm–5:50pm Wednesday, March 7, 2018
Location: LL20 A
Secondary topics:
Graphs and Time-series
Roger Barga (Amazon Web Services),
Nina Mishra (Amazon Web Services),
Sudipto Guha (Amazon Web Services),
Ryan Nienhuis (Amazon Web Services)
Average rating:
(5.00, 8 ratings)
Roger Barga, Nina Mishra, Sudipto Guha, and Ryan Nienhuis detail continuous machine learning algorithms that discover useful information in streaming data. They focus on explainable machine learning, including anomaly detection with attribution, the ability to reduce false positives through user feedback, and the detection of anomalies in directed graphs.
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5:10pm–5:50pm Wednesday, March 7, 2018
Location: LL20 C
Mike Conover (SkipFlag)
Average rating:
(5.00, 4 ratings)
Mike Conover offers an overview of the essential techniques for understanding and working with natural language. From off-the-shelf neural networks and snappy preprocessing libraries to architectural patterns for bulletproof productionization, this talk will be of interest to anyone who uses language on a regular basis.
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5:10pm–5:50pm Wednesday, March 7, 2018
Location: LL20 D
Balasubramanian Narasimhan (Stanford University),
John-Mark Agosta (Microsoft),
Philip Lavori (Stanford University)
Average rating:
(3.00, 2 ratings)
Clinical collaboration benefits from pooling data to train models from large datasets, but it's hampered by concerns about sharing data. Balasubramanian Narasimhan, John-Mark Agosta, and Philip Lavori outline a privacy-preserving alternative that creates statistical models equivalent to one from the entire dataset.
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5:10pm–5:50pm Wednesday, March 7, 2018
Location: LL21 B
Sergey Ermolin and Suqiang Song demonstrate how to use Spark BigDL wide and deep and neural collaborative filtering (NCF) algorithms to predict a user’s probability of shopping at a particular offer merchant during a campaign period. Along the way, they compare the deep learning results with those obtained by MLlib’s alternating least squares (ALS) approach.
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5:10pm–5:50pm Wednesday, March 7, 2018
Location: Expo Hall 1
Secondary topics:
Expo Hall
Rodney Mullen (Almost Skateboards)
Average rating:
(5.00, 2 ratings)
The essence of modern skating is learning tricks that couple with specific terrain. Activision’s video game franchise testifies to the nearly endless possibilities. Rodney Mullen offers a nuanced look at how skaters nudge the endpoints of disparate submovements to create new combinations that may shine a different light on ideas in machine learning—plus it’s a lot of fun.
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11:00am–11:40am Thursday, March 8, 2018
Location: LL20 A
Clare Gollnick (NS1)
Average rating:
(4.86, 7 ratings)
At the heart of the reproducibility crisis in the sciences is the widespread misapplication of statistics. Data science relies on the same statistical methodology as these scientific fields. Can we avoid the same crisis of integrity? Clare Gollnick considers the philosophy of data science and shares a framework that explains (and even predicts) the likelihood of success of a data project.
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11:00am–11:40am Thursday, March 8, 2018
Location: LL20 C
Evan Kriminger (ZestFinance)
Average rating:
(4.40, 5 ratings)
What does it mean to explain a machine learning model, and why is it important? Mike Ruberry offers an overview of several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques presents a different perspective, and their clever application can reveal new insights and solve business requirements.
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11:00am–11:40am Thursday, March 8, 2018
Location: LL20 D
Josh Wills (Slack)
Average rating:
(4.00, 3 ratings)
Josh Wills describes recent data science and machine learning projects at Slack.
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11:00am–11:40am Thursday, March 8, 2018
Location: LL21 B
Average rating:
(5.00, 1 rating)
Stolen credit cards are a major problem faced by many companies, including Uber. Karthik Ramasamy and Lenny Evans detail a new weapon against stolen credit cards that uses computer vision to scan credit cards, verifying possession of the physical card with basic fake card detection capabilities.
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11:00am–11:40am Thursday, March 8, 2018
Location: LL20 B
Secondary topics:
Graphs and Time-series
Ryan Boyd (Neo4j)
Average rating:
(5.00, 1 rating)
Ryan Boyd explains how he and his team reconstructed a subset of the Twitter network of Russian troll accounts and applied graph analytics to the data using the Neo4j graph database to uncover how these accounts were spreading fake news.
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11:50am–12:30pm Thursday, March 8, 2018
Location: LL20 A
Secondary topics:
Graphs and Time-series
Ram Shankar Siva Kumar (Microsoft (Azure Security Data Science))
Average rating:
(4.00, 1 rating)
How should you best debug a security data science system: change the ML approach, redefine the security scenario, or start over from scratch? Ram Shankar answers this question by sharing the results of failed experiments and the lessons learned when building ML detections for cloud lateral movement, identifying anomalous executables, and automating incident response process.
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11:50am–12:30pm Thursday, March 8, 2018
Location: LL20 C
Stephen O'Sullivan (Data Whisperers)
Average rating:
(4.25, 4 ratings)
Stephen O'Sullivan takes you along the data science journey, from onboarding data (using a number of data/object stores) to understanding and choosing the right data format for the data assets to using query engines (and basic query tuning). You'll learn some new skills to help you be more productive and reduce contention with the data engineering team.
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11:50am–12:30pm Thursday, March 8, 2018
Location: LL20 D
Ashivni Shekhawat (Lyft)
Average rating:
(3.00, 3 ratings)
Ashivni Shekhawat explains how Lyft uses a mix of online learning, optimization, and control theory to operate its ride-sharing marketplace at an efficient price point.
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11:50am–12:30pm Thursday, March 8, 2018
Location: LL21 B
Damage to the meniscus is a physically limiting injury that can lead to further medical complications. Automatically classifying this damage at the time of an MRI scan would allow quicker and more accurate diagnosis. Jennie Wang, Valentina Pedoia, Berk Norman, and Yulia Tell offer an overview of their classification system built with 3D convolutional neural networks using BigDL on Apache Spark.
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1:50pm–2:30pm Thursday, March 8, 2018
Location: LL20 A
Ram Sriharsha (Databricks)
Average rating:
(4.75, 4 ratings)
How do you scale geospatial analytics on big data? And while you're at it, can you make it easy to use while achieving state-of-the-art performance on a single node? Ram Sriharsha offers an overview of Magellan—a geospatial optimization engine that seamlessly integrates with Spark—and explains how it provides scalability and performance without sacrificing simplicity.
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1:50pm–2:30pm Thursday, March 8, 2018
Location: LL20 C
Average rating:
(5.00, 3 ratings)
Veronica Mapes and Garner Chung detail the human evaluation platform Pinterest developed to better serve its deep learning and operational teams when its needs grew beyond platforms like Mechanical Turk. Along the way, they cover tricks for increasing data reliability and judgement reproducibility and explain how Pinterest integrated end-user-sourced judgements into its in-house platform.
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1:50pm–2:30pm Thursday, March 8, 2018
Location: LL20 D
Jennifer Prendki (Figure Eight)
Average rating:
(3.00, 1 rating)
Jennifer Prendki explains how to develop machine learning models even if the data is protected by privacy and compliance laws and cannot be used without anonymizing, covering techniques ranging from contextual bandits to document vector representation.
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1:50pm–2:30pm Thursday, March 8, 2018
Location: Expo Hall 1
Secondary topics:
Expo Hall,
Graphs and Time-seriesAverage rating:
(5.00, 1 rating)
Many domains, such as mobile, web, the IoT, ecommerce, and more, have turned to analyzing streaming data. However, this presents challenges both in transforming the raw data to metrics and automatically analyzing the metrics in to produce the insights. Roy Ben-Alta and Ira Cohen share a solution implemented using Amazon Kinesis as the real-time pipeline feeding Anodot's anomaly detection solution.
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2:40pm–3:20pm Thursday, March 8, 2018
Location: LL20 A
Ian Cook (Cloudera)
Average rating:
(4.75, 4 ratings)
The popular R package dplyr provides a consistent grammar for data manipulation that can abstract over diverse data sources. Ian Cook shows how you can use dplyr to query large-scale data using different processing engines including Spark and Impala. He demonstrates the R package sparklyr (from RStudio) and the new R package implyr (from Cloudera) and shares tips for making dplyr code portable.
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2:40pm–3:20pm Thursday, March 8, 2018
Location: LL20 C
Pramit Choudhary (h2o.ai)
Average rating:
(5.00, 3 ratings)
Pramit Choudhary explores the usefulness of a generative approach that applies Bayesian inference to generate human-interpretable decision sets in the form of "if. . .and else" statements. These human interpretable decision lists with high posterior probabilities might be the right way to balance between model interpretability, performance, and computation.
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2:40pm–3:20pm Thursday, March 8, 2018
Location: LL20 D
Average rating:
(4.00, 1 rating)
Dice.com recently released several free career advisory tools for technology professionals, including a salary predictor, a tool that recommends the next skills to learn, and a career path explorer. Simon Hughes and Yuri Bykov offer an overview of the machine learning algorithms behind these tools and the technologies used to build, deploy, and monitor these solutions in production.
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2:40pm–3:20pm Thursday, March 8, 2018
Location: LL21 B
Patrick Harrison (S&P Global)
Average rating:
(4.33, 3 ratings)
Word vector embeddings are everywhere, but relatively few understand how they produce their remarkable results. Patrick Harrison opens up the black box of a popular word embedding algorithm and walks you through how it works its magic. Patrick also covers core neural network concepts, including hidden layers, loss gradients, backpropagation, and more.
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2:40pm–3:20pm Thursday, March 8, 2018
Location: Expo Hall 1
Secondary topics:
Expo Hall
Chris Fregly (Amazon Web Services)
Average rating:
(5.00, 1 rating)
Chris Fregly demonstrates how to extend existing Spark-based data pipelines to include TensorFlow model training and deploying and offers an overview of TensorFlow’s TFRecord format, including libraries for converting to and from other popular file formats such as Parquet, CSV, JSON, and Avro stored in HDFS and S3.
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4:20pm–5:00pm Thursday, March 8, 2018
Location: LL21 C/D
Kimoon Kim (Pepperdata),
Ilan Filonenko (Bloomberg LP)
Average rating:
(5.00, 1 rating)
There is growing interest in running Spark natively on Kubernetes, and Spark data is often stored in HDFS. Kimoon Kim and Ilan Filonenko explain how to make Spark on Kubernetes work seamlessly with HDFS by addressing challenges such as HDFS data locality and secure HDFS support.
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4:20pm–5:00pm Thursday, March 8, 2018
Location: LL20 A
Keno Fischer (Julia Computing)
Julia is rapidly becoming a popular language at the forefront of scientific discovery. Keno Fischer explores one of the most ambitious use cases for Julia: using machine learning to catalog astronomical objects to derive a catalog from multiterabyte size astronomical image datasets. This work was a collaboration between MIT, UC Berkeley, LBNL, and Julia Computing.
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4:20pm–5:00pm Thursday, March 8, 2018
Location: LL20 D
Goodman Gu (Cogito)
Average rating:
(5.00, 3 ratings)
Machine learning is a pivotal technology. However, bringing an ML application to life often requires overcoming bottlenecks not just in the model code but in operationalizing the end-to-end system itself. Goodman Gu shares a case study from a leading SaaS company that quickly and easily built, trained, optimized, and deployed an XGBoost churn prediction ML app at scale with Amazon SageMaker.
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4:20pm–5:00pm Thursday, March 8, 2018
Location: LL21 B
Jeff Dean (Google)
Average rating:
(4.89, 9 ratings)
The Google Brain team conducts research on difficult problems in artificial intelligence and builds large-scale computer systems for machine learning research, both of which have been applied to dozens of Google products. Jeff Dean highlights some of Google Brain's projects with an eye toward how they can be used to solve challenging problems.
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4:20pm–5:00pm Thursday, March 8, 2018
Location: 210 C/G
Felix Gorodishter (GoDaddy)
Average rating:
(3.00, 2 ratings)
GoDaddy ingests and analyzes over 100,000 data points per second. Felix Gorodishter discusses the company's big data journey from ingest to automation, how it is evolving its systems to scale to over 10 TB of new data per day, and how it uses tools like anomaly detection to produce valuable insights, such as the worth of a reminder email.
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