Yesterday at AWS Re:Invent, Amazon Web Services, Inc. (AWS) 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 build, train, deploy and manage their own machine learning models.
The company 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.
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 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 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As data sets and variables grow, customers have to repeat this process again and again as models become outdated and need to be continuously retrained to learn and evolve from new information.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.
Amazon says SageMaker is a fully-managed service that removes the heavy lifting and guesswork from each step of the machine learning process. It makes model building and training easier by providing pre-built development notebooks, popular machine learning algorithms optimized for petabyte-scale datasets and automatic model tuning.
Amazon claims SageMaker also simplifies and accelerates the training process, automatically provisioning and managing the infrastructure to both train models and run inference to make predictions using these models.
AWS DeepLens was designed to help developers get hands-on experience in building, training and deploying models by pairing a physical device with a broad set of tutorials, examples, source code and integration with familiar AWS services to support learning and experimentation.
“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,” said Swami Sivasubramanian, VP of Machine Learning, AWS.
“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.”
“We’ve deepened our relationship with AWS, adding them as an Official Technology Provider of the NFL and are excited to use Amazon SageMaker for our next-generation stats initiative,” said Michelle McKenna-Doyle, SVP and CIO, National Football League.
DigitalGlobe, provider of high-resolution Earth imagery, data and analysis, works with enormous amounts of data every day. “[We are] making it easier for people to find, access and run compute against our 100PB image library which is stored in the AWS cloud in order to apply deep learning to satellite imagery,” said Dr. Walter Scott, Chief Technology Officer of Maxar Technologies and founder of DigitalGlobe. “We plan to use Amazon SageMaker to train models against petabytes of earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe's Geospatial Big Data Platform (GBDX) users can just push a button, create a model and deploy it all within one scalable distributed environment at scale,” said Scott.
Matt Fryer, VP and Chief Data Science Officer of Hotels.com and Expedia Affiliate Network, said, "At Hotels.com, we are always interested in ways to move faster, to leverage the latest technologies and stay innovative. With Amazon SageMaker, the distributed training, optimized algorithms and built-in hyperparameter features should allow my team to quickly build more accurate models on our largest data sets, reducing the considerable time it takes us to move a model to production. It is simply an API call. Amazon SageMaker will significantly reduce the complexity of machine learning, enabling us to create a better experience for our customers, fast.”
Khalid Al-Kofahi, who leads Thomson Reuters center for AI and Cognitive Computing, commented, “For over 25 years we have been developing advanced machine learning capabilities to mine, connect, enhance, organize and deliver information to our customers, successfully allowing them to simplify and derive more value from their work. Working with Amazon SageMaker enabled us to design a natural language processing capability in the context of a question-answering application. Our solution required several iterations of deep learning configurations at scale using the [SageMaker] capabilities.”
For those developers who are not experts in machine learning, but are interested in using these technologies to build a new class of apps that exhibit human-like intelligence, Amazon Transcribe, Amazon Translate, Amazon Comprehend and Amazon Rekognition video aim to provide high-quality, high-accuracy machine learning services that are scalable and cost-effective.
"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,” said Swami Sivasubramanian, VP of Machine Learning, AWS. “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.”
Customers of these services include:
To learn more about AWS's machine learning services, visit: https://aws.amazon.com/machine-learning/