Скачать или смотреть 46. How do you do machine learning on AWS?

The public cloud gives you amazing machine learning powers with a low barrier to entry. Once you know where to begin, you can quickly build solutions to process images, video, text and audio, as well as structured data. In this episode we talk about the managed AI services that are available on AWS and that require zero machine learning expertise (Rekognition, Polly, Transcribe, Forecast, Personalise, Comprehend, Lex, Textract, Translate). We will also talk about services you can use to create and run your own custom models (SageMaker). We will finally cover some different use cases and some of the things you need to consider before you do machine learning in production.

Chapters:

00:00 Intro
00:50 What kind of Machine Learning are we talking about?
02:11 The 3 steps needed when doing ML
03:35 The managed AWS services for ML (Rekognition, Polly, Transcribe, Forecast, etc)
06:55 How do you customise these services for more specific use cases?
08:44 How to use these services programmatically?
09:36 How can you build your own models on AWS (SageMaker)?
14:00 How to put a Machine Learning model in production
16:09 Concerns with ML (Ethics, ML at the Edge)
19:50 Is it convenient to use AWS managed services or would you save money by building something from scratch yourself?
22:10 Other resources

In this episode, we mentioned the following resources:
AI as a Service book https://www.manning.com/books/ai-as-a...
Julien Simon’s YouTube channel:    / juliensimonfr  
Article by MIT researchers “Amazon’s Rekognition shows gender and ethnic bias”: https://venturebeat.com/2019/01/24/am...
Article “One year moratorium on police use of Rekognition”: https://www.aboutamazon.com/news/poli...

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Do you have any AWS questions you would like us to address?
Leave a comment here or connect with us on Twitter:
  / eoins  
  / loige  

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