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Favorite You can now use Amazon Elastic Inference to accelerate inference and reduce inference costs for PyTorch models in both Amazon SageMaker and Amazon EC2. PyTorch is a popular deep learning framework that uses dynamic computational graphs. This allows you to easily develop deep learning models with imperative and idiomatic
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Shared by AWS Machine Learning April 13, 2020
Favorite Amazon Polly is a cloud service that offers text-to-speech (TTS), a system that converts text input into a waveform, in a range of 61 voices in 29 languages by using advanced deep learning technologies. The Amazon Polly service supports companies in developing digital products that use speech synthesis for
Read More Shared by AWS Machine Learning April 7, 2020
Favorite With Amazon SageMaker Ground Truth, you can easily and inexpensively build accurately labeled machine learning (ML) datasets. To decrease labeling costs, SageMaker Ground Truth uses active learning to differentiate between data objects (like images or documents) that are difficult and easy to label. Difficult data objects are sent to
Read More Shared by AWS Machine Learning April 7, 2020
Favorite Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology as used by Amazon.com, without requiring any prior machine learning experience. Using Amazon Personalize, you can generate personalized recommendations for your users through a simple API interface. We are pleased
Read More Shared by AWS Machine Learning April 6, 2020
Favorite As a multiplayer game publisher, you may often need to either over-provision resources or manually manage compute allocation when launching or maintaining an online game to avoid long player wait times. You need to develop, configure, and deploy tools that help you monitor and control the compute allocation. This
Read More Shared by AWS Machine Learning April 2, 2020
Favorite Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like
Read More Shared by AWS Machine Learning April 2, 2020
Favorite When running deep learning models in production, balancing infrastructure cost versus model latency is always an important consideration. At re:Invent 2018, AWS introduced Amazon SageMaker Neo and Amazon Elastic Inference, two services that can make models more efficient for deep learning. In most deep learning applications, making predictions using
Read More Shared by AWS Machine Learning March 31, 2020
Favorite In the past decade, deep learning has advanced many different areas, such as computer vision and natural language processing. State-of-the-art models now achieve near-human performance in tasks such as image classification. Deep neural networks can achieve this because they consist of millions of parameters that you train on large
Read More Shared by AWS Machine Learning March 31, 2020
Favorite This post is co-written by Alexander Carlson, a machine learning engineer at Autodesk. Autodesk started its digital transformation journey years ago by moving workloads from private data centers to AWS services. The benefits of digital transformation are clear with generative design, which is a new technology that uses cloud
Read More Shared by AWS Machine Learning March 31, 2020
Favorite Starting Tuesday, March 24, 2020, NVIDIA GTC Digital is offering courses for you to learn AWS best practices to accomplish your ML goals faster and more easily. Registration is free, so register now. The following sessions are available from AWS: S22492: Train BERT in One Hour Using Massive Cloud
Read More Shared by AWS Machine Learning March 25, 2020