Mar 15–18, 2020

Schedule: ML Applied sessions

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11:00am12:30pm Tuesday, March 17, 2020
Location: 230 A
Chris Fregly (Amazon Web Services)
Join in to build real-world, distributed machine learning (ML) pipelines with Chris Fregly using Kubeflow, MLflow, TensorFlow, Keras, and Apache Spark in a Kubernetes environment. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: LL20D
Suneeta Mall (Nearmap)
Using Kubernetes as the backbone of AI infrastructure, Nearmap built a fully automated deep learning inference pipeline that's highly resilient, scalable, and massively parallel. Using this system, Nearmap ran semantic segmentation over tens of quadrillions of pixels. Suneeta Mall demonstrates the solution using Kubernetes in big data crunching and machine learning at scale. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL20D
Zak Hassan (Red Hat)
The number of logs increases constantly and no human can monitor them all. Zak Hassan employs natural language processing (NLP) for text encoding and machine learning (ML) methods for automated anomaly detection to construct a tool to help developers perform root cause analysis more quickly. He provides a means to give feedback to the ML algorithm to learn from false positives. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL20D
Ebrahim Safavi (Mist, a Juniper Company), Jisheng Wang (Mist Systems)
Anomaly detection models are essential to run data-driven businesses intelligently. At Mist Systems, the need for accuracy and the scale of the data impose challenges to build and automate ML pipelines. Ebrahim Safavi and Jisheng Wang explain how recurrent neural networks and novel statistical models allow Mist Systems to build a cloud native solution and automate the anomaly detection workflow. Read more.
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5:05pm5:45pm Tuesday, March 17, 2020
Location: LL20D
Secondary topics:  Security and Privacy
Nicola Corradi (DataVisor)
Fraudulent attacks like fake reviews, application fraud, and promotion abuse create a common pattern shared within coordinated malicious accounts. Nicola Corradi explains novel deep learning (DL) models that learned to detect suspicious patterns, leading to the individuation of coordinated fraud attacks on social, dating, ecommerce, financial, and news aggregator services. Read more.
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11:00am11:40am Wednesday, March 18, 2020
Location: LL20D
Jaya Susan Mathew (Microsoft)
With the need to cater to a global audience, there's a growing demand for applications to support speech identification, translation, and transliteration from one language to another. Jaya Mathew explores this topic and how to quickly use some of the readily available APIs to identify, translate, or even transliterate speech or text within your application. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL20D
Liqun Shao (Microsoft)
Liqun Shao leads you through a new GitHub repository to show you how data scientists without NLP knowledge can quickly train, evaluate, and deploy state-of-the-art NLP models. She focuses on two use cases with distributed training on Azure Machine Learning with Horovod: GenSen for sentence similarity and BERT for question-answering using Jupyter notebooks for Python. Read more.
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1:45pm3:15pm Wednesday, March 18, 2020
Location: 230 A
Laura Schornack (JPMorgan Chase)
Many pieces go into integrating machine learning models into an application. Laura Schornack details how to create the architecture for each piece so it can be delivered in an agile manner. Along the way, you'll learn how to integrate these pieces into an existing application. Read more.
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4:15pm4:55pm Wednesday, March 18, 2020
Location: LL20D
Nisha Muktewar (Cloudera Fast Forward Labs), Victor Dibia (Cloudera Fast Forward Labs)
In many business use cases, it's frequently desirable to automatically identify and respond to abnormal data. This process can be challenging, especially when working with high-dimensional, multivariate data. Nisha Muktewar and Victor Dibia explore deep learning approaches (sequence models, VAEs, GANs) for anomaly detection, performance benchmarks, and product possibilities. Read more.

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