If you are in the tech world or in the executive business space, you've probably heard the term "Deep Learning" being thrown around. Within the AWS Machine Learning ecosystem the specific deep learning tools are:

• Amazon Rekognition
• Amazon Comprehend
• Amazon Transcribe
• Amazon Personalize
• Amazon Translate

Amazon Rekognition

An AWS deep learning image and video computer vision processing algorithm. Without additional training required it offers:

• automated content moderation
• facial recognition
• sentiment analysis
• labeling/metadata
• text detection
• celebrity recognition
• video segment detection

Read more about Amazon Rekognition

Amazon Comprehend

A natural-language processing (NLP) deep learning algorithm used to uncover valuable insights and connections in text including:

• Key phrase extraction
• Sentiment analysis
• Targeted sentiment
• Entity Recognition
• Language Detection
• Event detection
• Syntax Detection
• Personally Identifiable Information (PII) detection

Read more about Amazon Comprehend

Amazon Transcribe

An AWS deep learning algorithm which does audio speech recognition & transcription. It can be paired with Amazon Comprehend to then analyze audio data.

Amazon Personalize

A powerful realtime deep learning recommendation engine which is used to power the Amazon store recommendations.

Amazon Translate

A deep learning neural machine based language translation (Spanish to English, etc) algorithm. As of this writing, Amazon Translate supports 75 languages, including everything from Spanish, German, Chinese, and English, to less common languages like Welsh and Mongolian.

Advantages of cloud-based deep learning

The advantage of using something within a cloud-based deep learning ecosystem (like AWS or one of the other majors providers) are:

1. Ready to run - you don't have to spend time finding where you are going to deploy your models or store your data and getting those things connected, or even waste time getting your local machine setup as a development hub. It's already ready to run.
2. Connected to the internet - if you are working on a local machine and you want to share your data or conclusions with the world, you have to deploy it somehow. If you are running in the cloud it can be as simple as changing permissions.
3. Collaborator friendly - since you platform is cloud-native, it's easy to collaborate with people.
4. Guard rails - when attempting something the first time, there are numerous was to mess it up. With a pre-built service, the number of ways to get it wrong are smaller. That's not to say it's easy, but it's certainly easier than a "roll your own" framework.

Amazon, Google, Facebook, Microsoft are deeply involved in developing the six or so open-source deep learning frameworks - and then using those platforms to underpin their commercial products. New branded cloud services are being released every year and additional features are added to existing products. And there are continuing to improve their algorithms and expand their feature sets every month.

AWS Machine Learning Certification

If you are looking to grow your career or pivot into the machine learning field, you might be find our article "Is the AWS Machine Learning Certification worth it?" a good place to start.