Sep 9–12, 2019

Schedule: Temporal data and time-series sessions

9:00am12:30pm Tuesday, September 10, 2019
Location: Almaden Ballroom (Hilton)
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Average rating: ***..
(3.50, 2 ratings)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from, MLS Listings, the World Bank, Baosight, and Midea/KUKA. Read more.
1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 C/D
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Average rating: ****.
(4.40, 5 ratings)
Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.
2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 C
Wei Cai (Cox Communications)
Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM. Read more.
4:50pm5:30pm Wednesday, September 11, 2019
Location: LL21 C/D
Anuradha Gali (Uber)
There are 15 million trips a day on the Uber platform. Anu Gali walks you through how Uber leverages AI to automate its business model via its unique platform. You'll learn about technology that evolves based on current market insights and dynamically adjusts for the future. She shares best practices and the architecture that enables organizations like Uber to grow and scale rapidly. Read more.
11:05am11:45am Thursday, September 12, 2019
Location: Expo Hall 3
Francesca Lazzeri (Microsoft)
Average rating: ***..
(3.50, 2 ratings)
Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and hyperparameter tuning. Read more.
11:55am12:35pm Thursday, September 12, 2019
Location: LL21 E/F
Average rating: ****.
(4.25, 4 ratings)
Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset. Read more.
11:55am12:35pm Thursday, September 12, 2019
Location: LL21 C/D
Ting-Fang Yen (DataVisor)
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems. Read more.
4:50pm5:30pm Thursday, September 12, 2019
Location: 230 A
Alex (Tianchu) Liang (American Tire Distributors)
Average rating: ****.
(4.00, 1 rating)
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling. Read more.

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