By Julien Simon
The podcast currently has 17 episodes available.
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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* 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.
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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/
* 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.
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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
* 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.
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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:
This podcast is also available in video at https://youtu.be/H8VjSI6Czy8
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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://youtu.be/sd6Ydg0wAPs
For 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.
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Additional resources mentioned in the podcast:
This podcast is also available in video at https://youtu.be/czngW9Wkjxw
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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/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.
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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.html
This 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.
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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
The podcast currently has 17 episodes available.