Podcasts about bigdl

In this week’s Conversations in the Cloud, we are joined by Esther Baldwin, Artificial Intelligence Solutions Architect at Intel. Esther talks about AI for Good and how she was inspired by her mother to make a difference in the world. She encourages young people entering the industry to have courage – to talk to experts and leaders, to learn and grow, and not to let them themselves be held back. Esther recommends following fellow Intel star Riva Tez at https://twitter.com/rivatez for inspiration on this. A Forrester study noted that pre-configured and verified IT infrastructure was a key strategy for addressing solution complexity. Esther notes that our work isn’t complete unless solutions are consumable by customers at scale. On the topic of HPC & AI converged clusters, there’s a perception that if you want to do AI, you must stand up a separate cluster, which Esther notes is not true. Existing HPC customers can do AI on their existing infrastructure with solutions like HPC & AI converged clusters. However, running three workloads – HPC, AI, analytics – together can be tough, with the main problem being that they all have their own software stack and libraries that are optimized for specific applications. With a shared infrastructure, it can be challenging to run these all at the same time. The queuing system creates difficulties and even the underlying file system is incompatible. This is where Intel® Select Solutions comes in. Intel Select Solutions help people leverage experience with a pre-configured path to get a faster time to value. Intel® Select Solutions for HPC & AI Clusters offers users a quick start for those in the HPC environment wanting to run AI workloads. There are two options for Intel Select Solutions for HPC & AI Clusters – an open source version with Magpie and Slurm and a commercially available version with Univa Grid Engine. Intel offers a family of Intel Select Solutions for HPC and AI. Building on the foundation of Intel® Select Solutions for Simulation & Modeling, customers can also utilize solutions for simulation & visualization and genomics analytics, in addition to AI solutions like BigDL on Apache Spark and AI Inferencing. Esther notes that the HPC & AI convergence is already a trend, with AI becoming part of a wide variety of workloads. This solution will enable new HPC and AI use cases, in addition to seeing lower total cost of operations, better cluster management, and stronger workload performance. More information on Intel Select Solutions and Intel HPC solutions is available at www.intel.com/selectsolutions and www.intel.com/hpc.

In this Intel Conversations in the Cloud audio podcast: On this week’s Conversations in the Cloud, we welcome Radhika Rangarajan, Engineering Director for Data Analytics and AI Ecosystem at Intel. Radhika offers an overview of BigDL, a distributed deep learning library for Apache Spark that enables efficient, scalable, and optimized deep learning development. People don’t […]

In this Intel Conversations in the Cloud audio podcast: On this week’s Conversations in the Cloud, we welcome Radhika Rangarajan, Engineering Director for Data Analytics and AI Ecosystem at Intel. Radhika offers an overview of BigDL, a distributed deep learning library for Apache Spark that enables efficient, scalable, and optimized deep learning development. People don’t […]

On this week’s Conversations in the Cloud, we welcome Radhika Rangarajan, Engineering Director for Data Analytics and AI Ecosystem at Intel. Radhika offers an overview of BigDL, a distributed deep learning library for Apache Spark that enables efficient, scalable, and optimized deep learning development. People don’t always know what ingredients to utilize to build their AI workloads. The new Intel Select Solution for BigDL on Apache Spark provides a verified configuration of hardware and software that enables users to more quickly build, test, and deploy solutions. Radhika also discusses the need for interoperability. The AI landscape has a broad array of different tools and libraries that ultimate need to coexist. Data scientists want their preferred libraries to scale on a big data stack, which doesn’t always work. Analytics Zoo helps scientists and engineers connect their libraries with an underlying Apache Spark ecosystem to help build deep learning models and workloads. Radhika also highlights her role in Women in Big Data, an organization she co-founded in 2015 to bring more women into the big data space through training, networking, and mentoring. The organization has now expanded into a global audience of more than 7500 women with more than a dozen chapters worldwide. For more information on BigDL and other libraries, visit http://software.intel.com/AI. Explore advanced analytics with Intel at www.intel.com/analytics, and learn more about Intel Select Solutions at www.intel.com/selectionsolutions.

In this episode of the Data Show, I spoke with Jason Dai, CTO of Big Data Technologies at Intel, and one of my co-chairs for the AI Conference in Beijing. I wanted to check in on the status of BigDL, specifically how companies have been using this deep learning library on top of Apache Spark, […]

In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark's ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Session sponsored by Intel