Amazon's Giant Push Into Machine Learning
Amazon's Giant Push Into Machine Learning
Written by Sue Gee   
Thursday, 30 November 2017

Amazon SageMaker, a fully managed service for the machine learning process; AWS DeepLens, a deep learning-enabled wireless video camera and four new machine learning application services were announced on the third day of this year's AWS re:Invent and are a direct challenge to Google and Microsoft.

Until now when we've put Amazon and Machine Learning together in the same sentence it has generally been in the context of recommendation systems. Indeed, when Amazon Machine Learning was introduced at 2015's AWS re:Invent recommendations based on purchases was used as the main illustration. This has changed overnight with a range of new products and services announced by AWS CEO Andy Jassy. Now Amazon is poised to take advantage of its position as the leading cloud provider to penetrate areas of machine learning and artificial intelligence currently dominated by Microsoft and Google.

Introducing SageMaker, Amazon uses a by now familiar refrain:

Today, implementing machine learning is complex, involves a great deal of trial and error, and requires specialized skills. Developers and data scientists must first visualize, transform, and pre-process data to get it into a format that an algorithm can use to train a model. 

We know how it goes on - training models needs  massive amounts of computer power and huge investment of time, a lot of effort and guesswork goes into each stage. Then deployment needs a different set of specialized skills.

A fully managed service, SageMaker is Amazon's solution for eliminating the:

"heavy lifting and guesswork from each step of the machine learning process"

 

sagemaker

 

It has three main components which can be used un isolation or as an end-to-end process:

  • Authoring: Zero-setup hosted Jupyter notebook IDEs for data exploration, cleaning, and preprocessing. You can run these on general instance types or GPU powered instances.
  • Model Training: A distributed model building, training, and validation service. You can use built-in common supervised and unsupervised learning algorithms and frameworks or create your own training with Docker containers. The training can scale to tens of instances to support faster model building. Training data is read from S3 and model artifacts are put into S3. The model artifacts are the data dependent model parameters, not the code that allows you to make inferences from your model.
  • Model Hosting: A model hosting service with HTTPs endpoints for invoking your models to get realtime inferences. These endpoints can scale to support traffic and allow you to A/B test multiple models simultaneously. Again, you can construct these endpoints using the built-in SDK or provide your own configurations with Docker images.

Commenting on SageMaker, Swami Sivasubramanian, VP of Machine Learning, AWS, said:

"Our original vision for AWS was to enable any individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world. Our vision for machine learning is no different, we want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level. Amazon SageMaker removes a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models." 

Andy Jassy's introduction to SageMaker has more details:

 

Sivasubramanian also explained how the four new machine learning applications are a natural extension to AWS:

"Today, customers are storing more data than ever before, using Amazon Simple Storage Service (Amazon S3) as their scalable, reliable, and secure data lake. These customers want to put this data to use for their organization and customers, and to do so they need easy-to-use tools and technologies to unlock the intelligence residing within this data. We're excited to deliver four new machine learning application services that will help developers immediately start creating a new generation of intelligent apps that can see, hear, speak, and interact with the world around them."

 

  • Amazon Transcribe (available in preview) converts speech to text, allowing developers to turn audio files stored in Amazon S3 into accurate, fully punctuated text. Trained to handle even low fidelity audio, such as contact center recordings, with a high degree of accuracy it can generate a time stamp for every word so that developers can precisely align the text with the source file. Initially it supports English and Spanish with more languages to follow. In the coming months the ability to recognize multiple speakers in an audio file will be added together with a facility to upload custom vocabulary for more accurate transcription.

  • Amazon Translate (available in preview) uses neural machine translation techniques to provide highly accurate translation of  text  (in both short- and long-form) from one language to another. It currently supports translation between English and six other languages (Arabic, French, German, Portuguese, Simplified Chinese, and Spanish), with many more to come in 2018.

  • Amazon Comprehend (available today) can understand natural language text from documents, social network posts, articles, or any other textual data stored in AWS. It uses deep learning techniques to identify text entities (e.g. people, places, dates, organizations), the language the text is written in, the sentiment expressed in the text, and key phrases with concepts and adjectives, such as 'beautiful,' 'warm,' or 'sunny.' Amazon Comprehend integrates with AWS Glue to enable end-to-end analytics of text data stored in Amazon data sources including S3, Redshift, RDS and DynamoDB.

  • Amazon Rekognition Video (available today) can track people, detect activities, and recognize objects, faces, celebrities, and inappropriate content in millions of videos stored in Amazon S3. It also provides real-time facial recognition across millions of faces for live stream videos and can automatically tag specific sections of video with labels and locations (e.g. beach, sun, child), detect activities (e.g. running, jumping, swimming), detect, recognize, and analyze faces, and track multiple people, even if they are partially hidden from view in the video.

This final service builds on Amazon Rekognition for still images that was launched at last year's AWS re:Invent. At the same time Amazon Polly, a text-to-speech service and Amazon Lex which combines speech recognition and natural language understanding and powers Amazon Alexa were introduced. Back then I noted that these services seemed like a catch-up to those provided by Microsoft. However, with AWS having a more developed cloud ecosystem Amazon now seems to be making an overtaking move on Microsoft Cognitive Services.

AWS DeepLens claims to to be the first of its kind:

 a deep-learning enabled, fully programmable video camera, designed to put deep learning into the hands of any developer, literally.

deeplens

 

AWS DeepLens, which is available for pre-order and will ship in the US in April 2018, is priced at $249, the same as Google's wearable camera called Clips that debuted last month and uses machine learning to identify people and pets of your choosing and only capture them. Price is the only feature in common. AWS DeepLens is an HD video camera with on-board compute capable of running over 100 billion deep learning operations per second. It comes with sample projects, example code, and pre-trained models to allow developers with no machine learning experience to run their first deep learning model in less than ten minutes. For example, AWS DeepLens could be programmed to recognize the numbers on a license plate and trigger a home automation system to open a garage door, or AWS DeepLens could recognize when the dog is on the couch and send a text to its owner.

AWS DeepLens integrates with Amazon SageMaker so that developers can train their models in the cloud with Amazon SageMaker and then deploy them to AWS DeepLens with just a few clicks in the AWS Management Console. 

Again, it is this cloud integration that could give Amazon an edge over its rivals and make it a prime player in AI.

 

More Information

Amazon SageMaker

AWS DeepLens

Related Articles

New AWS Services

Amazon AI Services

Amazon Rekognition Can Now Estimate Your Age

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Last Updated ( Thursday, 30 November 2017 )
 
 

   
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