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 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.

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.

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 As data sets and variables grow, customers have to repeat this process again and again as models become outdated and need to be continuously retrained to learn and evolve from new information.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.

Amazon SageMaker is a fully managed service that removes the heavy lifting and guesswork from each step of the machine learning process. Amazon SageMaker 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 SageMaker also dramatically 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 from the ground-up 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.”