AWS AI & Machine Learning Podcast

In this episode, I go through our latest announcements on Amazon Polly, Amazon Rekognition, Amazon Lex, Amazon Personalize, and Amazon SageMaker Ground Truth. I demo how to use a Lex chatbot for search queries on Kendra.

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Rekognition blog post: https://aws.amazon.com/blogs/media/streamline-media-analysis-tasks-with-amazon-rekognition-video/
Kendra blog post: ...

In this episode, I go through our latest announcements on Amazon Forecast, Amazon Code Guru, Amazon Transcribe, and the AWS Deep Learning Containers. I demo real-time profanity filtering with Transcribe (run for cover), and Elastic Inference with TensorFlow and SageMaker.

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For more content:
* AWS blog: https://aws.amazon.com/blogs/aws/auth...
* Medium blog: ht...

In this episode, I go through our latest announcements on AWS Amazon Augmented AI, Amazon SageMaker Studio, Amazon Sagemaker, and PyTorch.

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AWS blog posts mentioned in the podcast:
* https://aws.amazon.com/blogs/machine-learning/announcing-availability-of-inf1-instances-in-amazon-sagemaker-for-high-performance-and-cost-effective-machine-learning-inference/
* h...

In this episode, I go through our latest announcements on AWS Textract, Amazon Polly. AWS DeepLens, Amazon SageMaker, and the AWS Deep Learning Composers. A couple of small demos are included.

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For more content:
* AWS blog: https://aws.amazon.com/blogs/aws/auth...
* Medium blog: https://medium.com/@julsimon 
* YouTube: https://youtube.com/juliensimonfr 
* Po...

In this episode, I go through our latest announcements on AWS DeepComposer, Amazon Transcribe Medical, and Amazon Personalize.

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Additional resources mentioned in the podcast:

In this episode, I go through our latest announcements on Amazon Forecast, Amazon Personalize, Amazon SageMaker Ground Truth, AWS Deep Learning AMIs, and Amazon Elastic Inference.

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Additional resources mentioned in the podcast:

In this episode, I focus on Amazon Kendra, an enterprise search service powered by machine learning... but you don't need any ML skills to set it up and use it! I show you how to create an index, add data sources, and then I run queries using the AWS console and the AWS CLI.

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https://aws.amazon.com/kendra/

This podcast is also available in video at https...

In this episode, I go through our latest announcements on Amazon Transcribe, Amazon Rekognition, Amazon Forecast and the Deep Learning Containers. I do a couple of demos (redacting personal information in text transcripts, and extracting text from videos). Finally, I share a couple of SageMaker videos that I recently recorded.

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Additional resources mentioned...

In this episode, I talk about XGBoost 1.0, a major milestone for this very popular algorithm. Then, I discuss the three options you have for running XGBoost on Amazon SageMaker: built-in algo, built-in framework, and bring your own container. Code included, of course!

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Additional resources mentioned in the podcast:
* XGBoost built-in algo: https://gitlab.com/...

In this episode, I cover new features on Amazon Personalize (recommendation & personalization), Amazon Polly (text to speech), and Apache MXNet (Deep Learning). I also point out new notebooks for Amazon SageMaker Debugger, a couple of recent videos that I recorded, and an upcoming SageMaker webinar.

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Additional resources mentioned in the podcast:
* Amazon...

In this episode, I have a chat with Leo Souquet, a Data Scientist and the co-founder of the Data Science Tech Institute (https://www.datasciencetech.institute). DSTI trains Data Scientists and Data Engineers, and we chat about those two roles, their respective skills, how they’re related, and why you need both on your team to build successful projects. Much to my sorrow, Leo also convincingly explains why you have to learn math to ...

In this episode, I have a chat with Ségolène Dessertine-Panhard, a Data Scientist with the AWS Machine Learning Solutions Lab (https://aws.amazon.com/ml-solutions-lab/). As she spends her time working on customer projects, we talk about ML in the trenches: framing problems, building datasets, picking algos, deploying to production, and more.

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This podcast is...

In this episode, I have a chat with Francesco Pochetti, a Data Scientist who worked on Amazon Kindle for 4 years. He's also an AWS Machine Learning Hero. We talk about real life ML, how to deal with business stakeholders, why model explainability is so important, and more.

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Francesco's Twitter: @Fra_Pochetti
Francesco's blog: https://francesco...

In this episode, I have a chat with Cosmin Catalin Sanda, a Data Science Engineer for AudienceProject (https://www.audienceproject.com). We talk about real life ML, what it takes for ML projects to be successful (experimentation, agility, intuition, etc.), how SageMaker helps, and more.

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Cosmin's Twitter: https://twitter.com/cosminsanda
Cosmin's blog:...

In this episode, I talk about a couple of recent features, share some extra resources, and point you to pretty cool computer vision demo.

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News
* Amazon Comprehend - multi-label classification: https://aws.amazon.com/about-aws/whats-new/2020/01/amazon-comprehend-launches-multi-label-custom-classification/
* Amazon Translate - batch translation: https://aws.am...

In this episode, I have a chat with Pavlos Mitsoulis-Ntompos, a Data Scientist for the Expedia Group and an AWS Machine Learning Hero. We talk about real life ML, and what it takes for ML projects to be successful.

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Check out Sagify, an excellent CLI tool for Amazon SageMaker written by Pavlos:
* https://kenza-ai.github.io/sagify/
* End to end demo: https://y...

In this episode, I explain in plain English 26 Machine Learning and Deep Learning terms that you need to know. If you're beginning with the field, this should save you quite a bit of frustration!

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Audio samples have been generated with Amazon Polly :)

The podcast is hosted at https://julsimon.buzzsprout.com, and is also available on YouTube at https://bi...

In this episode, I focus on TensorFlow 2.0, the most popular open source library for Machine Learning. I explain in detail how it differs from TensorFlow 1.x, and how you can start using it on AWS: Deep Learning AMIs, Deep Learning Containers, and Amazon SageMaker.

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Additional resources:
* TensorFlow on AWS: https://aws.amazon.com/tensorflow
* My SageMaker not...

In this episode, I talk about new features on Amazon Lex (chatbots), Amazon Textract (text extraction), Amazon Personalize (recommendation & personalization), and Amazon Transcribe (speech to text). I also demo profanity filtering with Transcribe (run for cover!).

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Additional resources mentioned in the podcast:
* Amazon SageMaker Ground Truth demo: https:...