No longer limited to global technology enterprises and data science specialists, machine learning (ML) has entered the mainstream. Thanks to the cloud, the barriers to widespread use of machine learning are rapidly disappearing. The cloud brings together data, low-cost storage, security, and machine learning services along with high-performance, cost-effective CPU- and GPU-based compute instances, which are essential to machine learning success. The cloud also offers a pay-as-you-go cost model that further enables customers to control costs.
More recently, complex deep learning models consisting of multiple layers of deep neural networks that take inspiration from how the human brain functions necessitate even more powerful compute resources. These advanced models require secure, scalable, and cost-effective CPUs coupled with powerful GPUs, as well as gigabytes or terabytes of storage.
With the cloud, you can either choose fully managed services that automatically manage your infrastructure so you don’t need to worry about hardware and software maintenance, or you can opt for self-managed machine learning lifecycle management to benefit from the scale and the ability to customize infrastructure in a more hands-on way.
Whatever you choose, with the cloud, you don’t need to invest in all possible options upfront. Resources are available on demand and are always up to date and ready to provide you with purpose-built machine learning tools, compute, storage, networking, and the latest infrastructure innovations.
This article is posted at aws.amazon.com
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