14–17 Oct 2019

Schedule: Data, Data Networks, Data Quality sessions

Add to your personal schedule
10:1510:30 Wednesday, 16 October 2019
Location: King's Suite
Jeff Jonas (Senzing)
Average rating: ****.
(4.36, 11 ratings)
Entity resolution—determining “who is who” and “who is related to whom”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster. Read more.
Add to your personal schedule
11:0511:45 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Qun Ying (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and explain how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
Add to your personal schedule
13:4514:25 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Douglas Calegari (Independent)
Average rating: ****.
(4.00, 3 ratings)
Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system. Read more.
Add to your personal schedule
16:0016:40 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Average rating: ****.
(4.00, 2 ratings)
AI-powered market research is performed by indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases). These approaches are more scalable, authentic, and suitable for real-time consumer insights. Gianmario Spacagna proposes a novel algorithm of audience projection able to provide consumer insights over multiple domains. Read more.
Add to your personal schedule
9:309:45 Thursday, 17 October 2019
Location: King's Suite
Ihab Ilyas (University of Waterloo)
Average rating: ****.
(4.30, 10 ratings)
Ihab Ilyas highlights the data-quality problem and describes the HoloClean framework, a state-of-the-art prediction engine for structured data with direct applications in detecting and repairing data errors, as well as imputing missing labels and values. Read more.
Add to your personal schedule
11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Julien Simon (AWS)
Average rating: ****.
(4.86, 7 ratings)
Many natural language processing (NLP) tasks require each word in the input text to be mapped to a vector of real numbers. Julien Simon explores word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). You'll get to see how to solve typical NLP problems through several demos by either computing embeddings or reusing pretrained ones. Read more.
Add to your personal schedule
13:4514:25 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Sridhar Alla (BlueWhale)
Average rating: *....
(1.67, 3 ratings)
Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data. Read more.
Add to your personal schedule
16:0016:40 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Manas Ranjan Kar (Episource)
Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. It’s also crucial to understand to diagnose an NLP model performance and identify possible gaps. Read more.
Add to your personal schedule
16:0016:40 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Tuhin Sharma (Binaize), Bargava Subramanian (Binaize)
Average rating: ****.
(4.50, 2 ratings)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning—which is privacy preserving and doesn't require data to be moved to the cloud—for data quality and cybersecurity. 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

aisponsorships@oreilly.com

For information on exhibiting or sponsoring a conference

pr@oreilly.com

For media/analyst press inquires