Anand is the GM and Chief of Staff for Microsoft AI. Previously he was the Chief of Staff for Microsoft Azure Data Group covering Data Platforms and Machine Learning. In the last decade, he ran the product management and the development teams at Azure Data Services, Visual Studio and Windows Server User Experience teams at Microsoft. Anand holds a PhD in Computational fluid mechanics and worked several years as researcher before joining Microsoft.
April 24, 2019 05:00 PM PT
We present the Azure Cognitive Services on Spark, a simple and easy to use extension of the SparkML Library to all Azure Cognitive Services. This integration allows Spark Users to embed cloud intelligence directly into their spark computations, enabling a new generation of intelligent applications on Spark. Furthermore, we show that with our new Containerized Cognitive Services, one can embed cloud intelligence directly into the Spark cluster for ultra-low latency, on-prem, and offline applications.
We show how using our Integration, one can compose these cognitive services with other services, SQL computations, and Deep Networks to create sophisticated and intelligent heterogenous applications. Moreover, we show how to redeploy these compositions as Restful Services with Spark Serving. We will also explore the architecture of these contributions which leverage HTTP on Spark, a novel integration between Spark with the widely used Hypertext Transfer Protocol (HTTP). This library can integrate any framework into the Spark ecosystem that is capable of communicating through HTTP. Finally, we demonstrate how to use these services to create a large class of intelligent applications such as custom search engines, realtime facial recognition systems, and unsupervised object detectors.
October 22, 2021 04:30 PM PT
We present a novel deep learning approach to create a robust object detection network for use in an infra-red, UAV-based, poacher recognition system. More specifically we have used Microsoft AirSim to generate thousands of hours of simulated drone footage in the African Savannah. We then used deep domain adaptation to translate our simulation into a form that is adversarially indistinguishable from real infrared drone footage. This yields a programmable data generator that can be used to dramatically improve the accuracy of algorithms without requiring expensive human curated annotations. Furthermore, we extend this work and contribute a photorealism extension to AirSim, automating much of the domain specific expertise needed for computer graphics work, and enabling the generation of limitless quantities of photorealistic data for use in reinforcement learning and autonomous vehicles.
Session hashtag: #SAISDD2
October 22, 2021 04:30 PM PT
We present HTTP on Spark, a novel integration between Spark with the widely used Hypertext Transfer Protocol (HTTP). This library can be used to integrate any framework into the Spark ecosystem that is capable of communicating through HTTP. Furthermore, HTTP on Spark enables distributed and fault tolerant micro service architectures that commute with Spark’s dynamic allocation and Streaming capabilities. We build upon this work and release a library of idiomatic spark bindings for a wide array of Microsoft Cognitive Services. These bindings allow users to easily add *any* cognitive service as a part of their existing Spark and SparkML machine learning pipelines. Finally, we demonstrate how to use these services to create a large class of custom image classification and object detection systems that can learn without requiring human labeled training examples. We demonstrate the power of these new releases with an automated Snow Leopard Detection system.