23:39
Simplifying ML Workflows with Apache Beam
Tyler Akidau discusses how Apache Beam is simplifying pre- and post-processing for ML pipelines.
46:55
Understanding Software System Behavior with ML and Time Series Data
David Andrzejewski discusses how time series datasets can be combined with ML techniques in order to aid in the understanding of system behaviors in order to improve performance and uptime.
38:12
Analyzing & Preventing Unconscious Bias in Machine Learning
Rachel Thomas keynotes on three case studies, attempting to diagnose bias, identify some sources, and discusses what it takes to avoid it.
39:22
Models in Minutes not Months: AI as Microservices
Sarah Aerni talks about how Salesforce built an AI platform that scales to thousands of customers.
46:41
Understanding ML/DL Models using Interactive Visualization Techniques
Chakri Cherukuri discusses how to use visualization techniques to better understand machine learning and deep learning models.
49:17
Machine Intelligence at Google Scale
Guillaume LaForge presents pre-trained ML services such as Cloud Vision API and Speech API that works without any training, introducing Cloud AutoML.
48:55
Real-Time Data Analysis and ML for Fraud Prevention
Mikhail Kourjanski addresses the architectural approach towards the PayPal internally built real-time service platform, which delivers performance and quality of decisions.
49:01
End-to-End ML without a Data Scientist
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
42:26
Deep Learning for Science
Prabhat discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics, and the future of AI in powering scientific discoveries.
37:05
pDB: Scalable Prediction Infrastructure with Precision and Provenance
Balaji Rengarajan describes the platform built on the Celect’s pDB framework, providing multiple use cases such as online personalization, document classification, and geospatial anomaly detection.
50:45
The Black Swan of Perfectly Interpretable Models
Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.
32:58
Counting is Hard: Probabilistic Algorithms for View Counting at Reddit
Krishnan Chandra explains the challenges of building a view counting system at scale, and how Reddit used probabilistic counting algorithms to make scaling easier.
