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Episode 035 - Machine Learning and Serverless with Luca Bianchi

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Manage episode 460533876 series 3636979
Content provided by Sébastien Stormacq and Amazon Web Services. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sébastien Stormacq and Amazon Web Services or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode, Dave chats with Luca Bianchi, Chief Technology Officer at Neosperience and WizKey. Luca covers his career in Machine Learning, an overview of Machine Learning on AWS, Amazon Rekognition, Amazon Comprehend, Amazon Sagemaker, and AWS Glue. Luca also discusses Edge ML and what can be achieved using commodity hardware. Luca on Twitter: https://twitter.com/bianchiluca Dave on Twitter: https://twitter.com/thedavedev Luca on LinkedIn: https://www.linkedin.com/in/lucabianchipavia/ Luca on Medium: https://medium.com/@aletheia Amazon Rekognition: https://aws.amazon.com/rekognition/ Amazon Comprehend: https://aws.amazon.com/comprehend/ Amazon Sagemaker: https://aws.amazon.com/sagemaker/ AWS Glue: https://aws.amazon.com/glue Towards Data Science: https://towardsdatascience.com/ Clean or Dirty HVAC? Using Amazon SageMaker and Amazon Rekognition Custom Labels to Automate Detection: https://bit.ly/3rvpNDJ Building a Neural Network on Amazon SageMaker with PyTorch Lightning: https://bit.ly/3xAmxLc Subscribe: Amazon Music: https://music.amazon.com/podcasts/f8bf7630-2521-4b40-be90-c46a9222c159/aws-developers-podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669 Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjk5NDM2MzU0OS9zb3VuZHMucnNz Spotify: https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI TuneIn: https://tunein.com/podcasts/Technology-Podcasts/AWS-Developers-Podcast-p1461814/ RSS Feed: https://feeds.soundcloud.com/users/soundcloud:users:994363549/sounds.rss
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162 episodes

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Manage episode 460533876 series 3636979
Content provided by Sébastien Stormacq and Amazon Web Services. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sébastien Stormacq and Amazon Web Services or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode, Dave chats with Luca Bianchi, Chief Technology Officer at Neosperience and WizKey. Luca covers his career in Machine Learning, an overview of Machine Learning on AWS, Amazon Rekognition, Amazon Comprehend, Amazon Sagemaker, and AWS Glue. Luca also discusses Edge ML and what can be achieved using commodity hardware. Luca on Twitter: https://twitter.com/bianchiluca Dave on Twitter: https://twitter.com/thedavedev Luca on LinkedIn: https://www.linkedin.com/in/lucabianchipavia/ Luca on Medium: https://medium.com/@aletheia Amazon Rekognition: https://aws.amazon.com/rekognition/ Amazon Comprehend: https://aws.amazon.com/comprehend/ Amazon Sagemaker: https://aws.amazon.com/sagemaker/ AWS Glue: https://aws.amazon.com/glue Towards Data Science: https://towardsdatascience.com/ Clean or Dirty HVAC? Using Amazon SageMaker and Amazon Rekognition Custom Labels to Automate Detection: https://bit.ly/3rvpNDJ Building a Neural Network on Amazon SageMaker with PyTorch Lightning: https://bit.ly/3xAmxLc Subscribe: Amazon Music: https://music.amazon.com/podcasts/f8bf7630-2521-4b40-be90-c46a9222c159/aws-developers-podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669 Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjk5NDM2MzU0OS9zb3VuZHMucnNz Spotify: https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI TuneIn: https://tunein.com/podcasts/Technology-Podcasts/AWS-Developers-Podcast-p1461814/ RSS Feed: https://feeds.soundcloud.com/users/soundcloud:users:994363549/sounds.rss
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162 episodes

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