AWS AI & Machine Learning Podcast

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

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.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Rekognition blog post: https://aws.amazon.com/blogs/media/streamline-media-analysis-tasks-with-amazon-rekognition-video/Kendra blog post: https://aws.amazon.com/blogs/aws/reinventing-enterprise-search-amazon-kendra-is-now-generally-available/SageMaker Ground Truth blog post: https://aws.amazon.com/blogs/aws/new-label-3d-point-clouds-with-amazon-sagemaker-ground-truth/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/@julsimon

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/@julsimon

In this episode, I go through our latest announcements on AWS Amazon Augmented AI, Amazon SageMaker Studio, Amazon Sagemaker, and PyTorch.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️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/* https://aws.amazon.com/blogs/aws/announcing-torchserve-an-open-source-model-server-for-pytorch/ 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/@julsimon

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.⭐️⭐️⭐️ 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/@julsimon

In this episode, I go through our latest announcements on AWS DeepComposer, Amazon Transcribe Medical, and Amazon Personalize.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Additional resources mentioned in the podcast:AWS DeepComposer blog post: https://aws.amazon.com/blogs/aws/aws-deepcomposer-now-generally-available-with-new-features/ Amazon Transcribe Medical demo: https://www.youtube.com/watch?v=_FoA5DZn7M8Amazon Personalize blog post: https://aws.amazon.com/blogs/machine-learning/introducing-recommendation-scores-in-amazon-personalize/For more content:AWS blog: https://aws.amazon.com/blogs/aws/author/julsimonMedium blog: https://medium.com/@julsimon YouTube: https://youtube.com/juliensimonfr Podcast: http://julsimon.buzzsprout.com Twitter https://twitter.com/@julsimon

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.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Additional resources mentioned in the podcast:AWS Solutions: https://aws.amazon.com/solutions/Amazon SageMaker Ground Truth end to end demo: https://www.youtube.com/watch?v=oEcH8amMcT8Amazon Elastic Inference: https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/amazon-elastic-inference/This podcast is also available in video at https://youtu.be/H8VjSI6Czy8 For more content:AWS blog: https://aws.amazon.com/blogs/aws/author/julsimonMedium blog: https://medium.com/@julsimon YouTube: https://youtube.com/juliensimonfr Podcast: http://julsimon.buzzsprout.com Twitter https://twitter.com/@julsimon

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. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️https://aws.amazon.com/kendra/This podcast is also available in video at https://youtu.be/sd6Ydg0wAPsFor more content, follow me on:* Medium https://medium.com/@julsimon* Twitter https://twitter.com/@julsimon

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-information/Amazon Rekognition Video code: https://gitlab.com/juliensimon/aws/-/blob/master/AmazonAI/rekognitionvideo/textinimage.py DeepAR research paper: https://arxiv.org/abs/1704.04110Train with Amazon SageMaker on your local machine: https://www.youtube.com/watch?v=K3ngZKF31mcAmazon SageMaker Studio Deep Dive: https://www.youtube.com/watch?v=pGhn8Ax8QmQ This podcast is also available in video at https://youtu.be/czngW9WkjxwFor more content, follow me on:Medium https://medium.com/@julsimonTwitter https://twitter.com/@julsimon

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-learn: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb * Deploying XGBoost with mlflow: https://youtu.be/jpZSp9O8_ew * New model format: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html* Converting pickled models: https://github.com/dmlc/xgboost/blob/master/doc/python/convert_090to100.py This podcast is also available in video at https://youtu.be/w0F4z0dMdzI.For more content, follow me on:* Medium https://medium.com/@julsimon* Twitter https://twitter.com/@julsimon

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.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Additional resources mentioned in the podcast:* Amazon Polly Brand Voice: https://aws.amazon.com/blogs/machine-learning/build-a-unique-brand-voice-with-amazon-polly/* Amazon SageMaker Debugger notebooks: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-debugger* Numpy for Apache MXNet: https://medium.com/apache-mxnet/a-new-numpy-interface-for-apache-mxnet-incubating-dbb4a4096f9f* Automating Amazon SageMaker workflows with AWS Step Functions: https://www.youtube.com/watch?v=0kMdOi69tjQ* Deploying Machine Learning Models with mlflow and Amazon SageMaker: https://www.youtube.com/watch?v=jpZSp9O8_ew* SageMaker webinar on February 27th: https://pages.awscloud.com/AWS-Online-Tech-Talks_2020_0226-MCL.htmlThis podcast is also available in video: https://youtu.be/KE83Aw6UvHk For more content, follow me on:* Medium https://medium.com/@julsimon * Twitter https://twitter.com/@julsimon

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-Vjvk.For more content, follow me on:- Medium: https://medium.com/@julsimon- Twitter: https://twitter.com/julsimon

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.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️This podcast is also available in video at https://youtu.be/74LwQP9oKU4For more content, follow me on:- Medium: https://medium.com/@julsimon- Twitter: https://twitter.com/julsimon

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_PochettiFrancesco's blog: https://francescopochetti.com/This podcast is also available in video at https://youtu.be/DQh9FQxwvhIFor more content, follow me on:* Medium: https://medium.com/@julsimon* Twitter: https://twitter.com/julsimon

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/cosminsandaCosmin'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/YqRd4Gh9a98For more content, follow me on:* Medium: https://medium.com/@julsimon* Twitter: https://twitter.com/julsimon

In this episode, I talk about a couple of recent features, share some extra resources, and point you to pretty cool computer vision demo.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️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.amazon.com/about-aws/whats-new/2020/01/amazon-translate-introduces-batch-translation/ Extra resources* O’Reilly AI 2018 talk (SageMaker built-in algos): https://youtu.be/dGn1HAHbLWM* O’Reilly AI 2019 talk (Introduction to NLP models): https://youtu.be/kwHGlTi27LY * Sagify demo: https://youtu.be/cWv8zR2Qu94Detectron2 demohttps://youtu.be/7R8-VAk0ruk This podcast is also available in video at https://youtu.be/RE3gD1ti_0sFor more content, follow me on:* Medium: https://medium.com/@julsimon* Twitter: https://twitter.com/julsimon

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=PLJgojBtbsuc3HZ55IiEMD0gF1tPZo02njFor more content, follow me at https://medium.com/@julsimon and at https://twitter.com/julsimon.

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! ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️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://bit.ly/368Iz7g. For more content, follow me at https://medium.com/@julsimon and at https://twitter.com/julsimon.

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, the creator of Keras: https://colab.research.google.com/drive/1UCJt8EYjlzCs1H1d1X0iDGYJsHKwu-NOThis podcast is also available in video: https://www.youtube.com/watch?v=Kqd7__Yllr0For more content, follow me at https://medium.com/@julsimon and at https://twitter.com/julsimon.

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/2bfxnj1J00AThis podcast is also available in video: https://www.youtube.com/watch?v=BretMPkpipgFor more content, follow me at https://medium.com/@julsimon and at https://twitter.com/@julsimon