Loading...
You are here:  Home  >  Education Resources For Use & Management of Data  >  Data Daily | Data News  >  Current Article

AWS Announces Five New Machine Learning Services Including SageMaker, DeepLens

By   /  December 4, 2017  /  No Comments

by Angela Guess

A recent press release states, “Today at AWS Re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company, announced five new machine learning services and a deep learning-enabled wireless video camera for developers. Amazon SageMaker is a fully managed service for developers and data scientists to quickly build, train, deploy, and manage their own machine learning models. AWS also introduced AWS DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning. And, AWS announced four new application services that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time.”

The release goes on, “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. Even simple models can require massive amounts of compute power and a great deal of time to train, and companies may need to hire dedicated teams to manage training environments that span multiple GPU-enabled servers. All of the phases of training a model—from choosing and optimizing an algorithm, to tuning the millions of parameters that impact the model’s accuracy—involve a great deal of manual effort and guesswork. Then, deploying a trained model within an application requires a different set of specialized skills in application design and distributed systems. As data sets and variables grow, customers have to repeat this process again and again as models become outdated and need to be continuously retrained to learn and evolve from new information. All of this takes a lot of specialized expertise, access to massive amounts of compute power and storage, and a great deal of time. To date, machine learning has been out of reach for most developers.”

Read more at Business Wire.

Photo credit: AWS

You might also like...

To Get Value from Data, Organizations Should Also Focus on Data Flow

Read More →