Schedule: Temporal data and time-series analytics sessions

Francesca Lazzeri and Jen Ren walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.

Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. Join in to discover what makes RNNs so powerful for time series analysis. Read more.

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 JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA. Read more.

Jian Chang and Sanjian Chen outline the design of the AI engine on Alibaba's TSDB service, which enables fast and complex analytics of large-scale retail data. They then share a successful case study of the Fresh Hema Supermarket, a major “new retail” platform operated by Alibaba Group, highlighting solutions to the major technical challenges in data cleaning, storage, and processing. Read more.

Dilated neural networks are a class of recently developed neural networks that achieve promising results in time series forecasting. Chenhui Hu discusses representative network architectures of dilated neural networks and demonstrates their advantages in terms of training efficiency and forecast accuracy by applying them to solve sales forecasting and financial time series forecasting problems. Read more.

Time series forecasting techniques are applied in a wide range of scientific disciplines, business scenarios, and policy settings. Jeffrey Yau discusses the applications of statistical time series models, such as ARIMA, VAR, and regime-switching models, and machine learning models, such as random forest and neural network-based models, to forecasting problems. Read more.

Ting-Fang Yen details an approach for monitoring production machine learning systems that handle billions of requests daily by discovering detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. Join in to explore new tools for detecting undesirable model behaviors early in large-scale online ML systems. Read more.

Jeff Chen shares strategies for overcoming time series challenges at the intersection of macroeconomics and data science, drawing from machine learning research conducted at the Bureau of Economic Analysis aimed at improving its flagship product the gross domestic product. Read more.

Malicious DNS traffic patterns are inconsistent and typically thwart anomaly detection. David Rodriguez explains how Cisco uses Apache Spark and Stripe’s Bayesian inference software, Rainier, to fit the underlying time series distribution for millions of domains and outlines techniques to identify artificial traffic volumes related to spam, malvertising, and botnets (masquerading traffic). Read more.

Machine learning is delivering immense value across industries. However, in some instances, machine learning models can produce overconfident results—with the potential for catastrophic outcomes. Kumar Sricharan explains how to address this challenge through Bayesian machine learning and highlights real-world examples to illustrate its benefits. Read more.

Deep learning using sequence-to-sequence networks (Seq2Seq) has demonstrated unparalleled success in neural machine translation. A less explored but highly sought-after area of forecasting can leverage recent gains made in Seq2Seq networks. Aashish Sheshadri explains how PayPal has applied deep networks to monitoring and alerting intelligence. Read more.

Anomaly detection has many applications, such as tracking business KPIs or fraud spotting in credit card transactions. Unfortunately, there's no one best way to detect anomalies across a variety of domains. Jonathan Merriman and Cynthia Freeman introduce a framework to determine the best anomaly detection method for the application based on time series characteristics. Read more.

Alex Gorbachev and Paul Spiegelhalter use the example of a mining haul truck to explain how to map preventive maintenance needs to supervised machine learning problems, create labeled datasets, do feature engineering from sensors and alerts data, evaluate models—then convert it all to a complete AI solution on Google Cloud Platform that's integrated with existing on-premises systems. Read more.