Domestic honey bee hives are collapsing at an alarming rate. Can technology help save the honey bee?
In this real-world environmental analytics opportunity, we explore how a variety of deep learning and machine learning technologies — including the Kinetica insight engine deployed to an NVIDIA DGX Station and the Microsoft Cognitive Toolkit deep learning framework – can deliver game-changing possibilities for innovation.
In this webinar, you will learn how new accelerated analytics technologies and their corresponding compute platforms can deliver this innovation as we follow a honey bee farm scientist in California, who agreed to field test this real-time monitoring solution with her beehives.
We’ll take a deep dive into honey bee hive health monitoring with NVIDIA’s TX2, TensorRT, Kinetica’s insight engine running on DGX-1/DGX Station, and Microsoft Cognitive Toolkit to rapidly optimize, validate, and deploy trained neural networks for inference.
See first-hand how adaptable and accessible these complex, cutting-edge technologies have become and how we can use intelligent monitoring technologies to help rescue the honey bee in the real world.
Join Jonathan Greenberg – Senior Solutions Engineer at Kinetica; Jacci Cenci – Solutions Architect, NVIDIA; and Anusua Trivedi – Data Scientist, Microsoft’s Cloud AI + Research Team for this webinar and you will:
- Discover how a modern GPU-powered insight engine tackles sensor data streaming with live querying.
- Learn about the convergence of AI and BI from a single platform.
- See how new accelerated analytics technologies, and their corresponding compute platforms, can deliver game-changing possibilities for real world innovation.
About the Speakers:
Jonathan Greenberg, Senior Solutions Engineer, Kinetica
Jonathan Greenberg is a Senior Solutions Engineer at Kinetica. Prior to Kinetica, Jonathan spent three years with startup companies exploring modern and innovative analytic technologies and platforms. He revels in the pace of change in software and hardware for analytics, the introduction of the GPU enhanced database, and the business impact around convergence of ML and BI that Kinetica brings to this challenged big data (science) space. Prior to that, Jonathan worked for Cognos, BMW, and IBM, over a twenty year career conceiving, developing, and delivering effective business intelligence solutions for a broad range of industries.
Jacci Cenci, Solutions Architect, NVIDIA
Jacci Cenci is a Solutions Architect at NVIDIA. She has worked for the past year at NVIDIA with partners and customers to support accelerated computing and deep learning requirements. Prior to NVIDIA, Jacci spent four years as a datacenter consultant focused on machine learning, big data analytics, technical computing, and enterprise solutions at Dell EMC. She has worked at General Atomics, Bright Computing, SGI, University of California, Irvine, NASA Ames, Netapp, Cadence Design Systems, and Digital Equipment Corp.
Anusua Trivedi, Data Scientist, Microsoft
Anusua Trivedi is a Data Scientist at Microsoft’s Cloud AI + Research Team. Anusa works on developing advanced deep learning models & AI solutions for clients and partners. She is an advanced trainer and conducts hands-on deep learning labs. Prior to joining Microsoft, Anusua held positions with UT Austin and University of Utah. Anusua is a frequent speaker at machine learning and AI conferences.
