Introduction
No longer limited to global technology enterprises and
data science specialists, machine learning 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 mimic
how a human brain functions necessitate even more
powerful compute resources. Powerful GPUs coupled
with CPUs, as well as the gigabytes or terabytes of
storage advanced models require, must be managed in a
secure, scalable, and cost-effective way to earn and keep
customer trust.
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 security of the cloud while customizing 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, storage, networking, and the latest
infrastructure innovations.
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