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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Additional resources: * TensorFlow on AWS: https://aws.amazon.com/tensorflow * My SageMaker notebook: https://gitlab.com/juliensimon/dlnotebooks/tree/master/keras/07-keras-fmnist-tf20 * A notebook giving you an overview of TensorFlow 2.0, by François Chollet ..read more

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Francesco's Twitter: @Fra_Pochetti Francesco's blog: https://francescopochetti.com/ This podcast is also available in video at https://youtu.be/DQh9FQxwvhI For more content, follow me on: * Medium: https://medium.com/@julsimon * Twitter: https://twi ..read more

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Cosmin's Twitter: https://twitter.com/cosminsanda Cosmin's blog: https://cosminsanda.com/ Apache MXNet User Group in Copenhagen: https://www.meetup.com/meetup-group-bdEUVQHL/ This podcast is also available in video at https://youtu.be ..read more

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!). ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Additional resources mentioned in the podcast: * Amazon SageMaker Ground Truth demo: https://www.youtube.com/watch?v=oEcH8amMcT8 * Amazon Textract demo: https://youtu.be/8kobcRynTTA * Introduction to Graph Convolutional Networks: https://youtu.be/2bfxnj1J ..read more

In this episode, I go through our November announcements on all AI/ML services. This is a great way to catch up before AWS re:Invent starts ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ For more content: * AWS blog: https://aws.amazon.com/blogs/aws/auth... * Medium blog: https://julsimon.medium.com * YouTube: https://youtube.com/juliensimonfr  * Podcast: http://julsimon.buzzsprout.com  * Twitter https://twitter.com/@julsimon ..read more

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Check out Sagify, an excellent CLI tool for Amazon SageMaker written by Pavlos: * https://kenza-ai.github.io/sagify/ * End to end demo: https://youtu.be/cWv8zR2Qu94 This podcast is also available in video: https://www.youtube.com/playlist?list=PLJgojBtbsuc3HZ55IiEMD0gF1tPZo02nj For more content, follow me a ..read more

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! ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Additional resources mentioned in the podcast: * XGBoost built-in algo: https://gitlab.com/juliensimon/ent321 * XGBoost built-in framework: https://gitlab.com/juliensimon/dlnotebooks/-/blob/master/sagemaker/09-XGBoost-script-mode.ipynb * BYO with Scikit-l ..read more

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 build the best models. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ This podcast is also available in video at https://youtu.be/llSjMi-V ..read more

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ For more content: * AWS blog: https://aws.amazon.com/blogs/aws/auth... * Medium blog: https://medium.com/@julsimon  * YouTube: https://youtube.com/juliensimonfr  * Podcast: http://julsimon.buzzsprout.com  * Twitter https://twitter.com ..read more

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️ Additional resources mentioned in the podcast: Amazon Transcribe blog post: https://aws.amazon.com/blogs/aws/now-available-in-amazon-transcribe-automatic-redaction-of-personally-identifiable-infor ..read more