Data By the Bay has ended

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Data By the Bay

Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas spanned by multiple horizontal data pipelines, platforms, and algorithms. We are unifying data science and data engineering, showing what really works to run businesses at scale.

Magnus Bjelkenhed

Arbetsförmedlingen Public Employment Services
Software Architect
Stockholm, Sweden

arbetsformedlingen.se

Machine Learning, Deep Learning, NLP, Apache Spark, Data Analysis

8:45am PDT

Grand Welcome & Opening Remarks GardnerAlexy Khrabrov

8:55am PDT

Host Sponsor Welcome: Nitro GardnerRoland Tritsch

9:05am PDT

Keynote: Building a Real-time Streaming Platform Using Kafka Streams and Kafka Connect GardnerJay Kreps

9:50am PDT

Time-series Feature Engineering Done Right GardnerParag Namjoshi Elastic Data Pipelines AdaClaudio Caldato • Adam Mollenkopf • Sunil Shah

10:40am PDT

Google BigQuery: a fully-managed data analytics service in the cloud GardnerMichael Entin

11:40am PDT

Functional Programming for Machine Learning and Data Pipelines GardnerMalcolm Greaves

1:10pm PDT

Akka Streams for Large Scale Data Processing AdaZack Loebel-Begelman • Dustin Lyons

2:10pm PDT

Real-time, Streaming Advanced Analytics, Approximations, and Recommendations using Apache Spark ML/GraphX, Kafka Stanford CoreNLP, and Twitter Algebird BONUS: Netflix Recommendations: Then and Now MarkovChris Fregly

3:00pm PDT

A Real Time Analytics Framework AdaHiral Patel

4:00pm PDT

Quantitative Trading with Machine Learning AdaMartin Froehler The Engineer's Guide to Streaming -- How you should really compare Storm, Flink, and Apex GardnerIlya Ganelin

5:00pm PDT

Panel: Future of Data Services GardnerAlexy Khrabrov • Marius Eriksen • Joel Horwitz • Haoyuan (H.Y.) Li • William Morgan • Neha Narkhede • Roman Shaposhnik

6:00pm PDT

Happy Hour The Galvanize Lobby

8:45am PDT

Opening Remarks GardnerAlexy Khrabrov

8:55am PDT

Host Sponsor Welcome: IBM Analytics GardnerJoel Horwitz

9:05am PDT

Data and Algorithmic Bias in the Web GardnerRicardo Baeza-Yates

9:50am PDT

Building Word2Vec Models with Text Data AdaJorge Castanon Identifying Actionable Messages on Social Media MarkovNemanja Spasojevic

10:40am PDT

lda2vec AdaChristopher Erick Moody Smarter Search with Spark, Solr and Machine Learning GardnerGrant Ingersoll

11:10am PDT

Image Retrieval using Short Texts MarkovMaarten Bosma

11:40am PDT

Using Spark MLlib for NLP MarkovMichelle Casbon

1:10pm PDT

Scalably Internationalizing Millions of Latent Semantic Labels MarkovXiao Fan

1:40pm PDT

Of Rules and Probabilities: Computational Linguistics Methods for Improving Machine Learning Models AdaVita Markman

2:10pm PDT

Aspect Based Sentiment Analysis in 20+ Languages AdaAntonio Valderrabanos

4:00pm PDT

Mining Noisy Transaction Data with Neural Nets AdaFrank Taylor

8:45am PDT

Opening Remarks GardnerAlexy Khrabrov

9:00am PDT

Let's Build Civic Tech to Scale Democracy for a Better Future GardnerMatt Mahan

9:50am PDT

Black Magic: How to apply Machine Learning to real-world problems MarkovEvion Kim Synthesizing human and machine capabilities AdaEric Colson

11:40am PDT

Unsupervised NLP Classification with Clustering GardnerMatthew Seal

1:10pm PDT

Case Law and ML on Spark AdaJeremy Corbett

2:10pm PDT

Building a Graph of all US businesses using Spark technologies AdaAlexis Roos Why The Best Minds Of Our Generation Are Thinking About How To Get People to Click on Ads GardnerJosh Wills

9:00am PDT

Keynote: How Can We Trust Machine Learning? Exploration, Evaluation and Explanation for ML Models GardnerCarlos Guestrin

9:50am PDT

What Kaggle has learned from 2MM machine learning models GardnerAnthony Goldbloom

10:40am PDT

Analyzing Massive Time Series Data with Spark GardnerSandy Ryza The Promise of Heterogeneous Computing MarkovSubbu Rama

11:10am PDT

Know the air you are breathing AdaSeema Jethani

11:40am PDT

Recommendations for Building Machine Learning Software AdaJustin Basilico

1:10pm PDT

Fast deep recurrent net training GardnerSanjeev Satheesh

2:10pm PDT

Scalable Training of RNNs for Speech Recognition GardnerErich Elsen

3:00pm PDT

Hidden GEMMs: How Optimized Math Libraries Work AdaMarek Kolodziej

3:30pm PDT

A Match Made at Upwork MarkovAlya Abbott

4:00pm PDT

Multimodal Question Answering for Language and Vision GardnerRichard Socher