Propel 4 common machine learning use cases into production

Prioritizing infrastructure decisions is essential to getting your ML models into production at scale and at optimal cost. After all, once you’ve determined that machine learning will enhance your business (through reduced costs, increased employee productivity, or improved customer experience) you’ll want to act quickly and purposefully.

But how can you really ensure that you have adequate infrastructure to support the compute, network, and storage needs of these common ML use cases? The fact is, the training of deep learning models for use cases such as natural language processing (NLP) and computer vision may require many months of time.

This eBook provides practical insights for setting up your infrastructure to enable any of the ML use cases identified throughout.

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Q&A: Choosing the right compute infrastructure for machine learning
Q&A: Choosing the right compute infrastructure for machine learning

Accelerate machine learning with low-cost, high-performance, ML-optimized infrastructure.

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AWS re:Invent 2021 - Ready, set, operate: The AWS Cloud operations model
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