MIDDLE EAST, PURE
or Middle East enterprises looking to implement AI or
Machine Learning projects, the good news is that the compute
bottleneck that used to hold back projects like these has largely
been eliminated. In recent years advancements in deep learning,
GPUs and Big Data have allowed AI to flourish. As a result, the
challenge for many projects is now providing the data fast enough to
feed the data analysis pipelines central to AI.
Until recently, enterprises were stuck with building their own
infrastructure to feed the AI pipeline. With DIY solutions, these
enterprises lost months of productivity going through the painful
cycle of integrating, testing and continuously maintaining both the
hardware and ever-evolving software. Even when all of this is done,
users often find that workloads are slow and data ends up in silos.
It’s because the system is built with old, legacy components cobbled
together in the system.
Bringing together data into a single centralised modern data
hub – as part of a deep learning architecture – enables far more
efficient access to information, increasing the
productivity of data scientists and making
scaling and operating simpler and more agile
for the data architect.
Modern all-flash based data platforms are
ideal candidates to act as that central data
hub. It’s an effective technology capable of
underpinning and releasing the full potential
of projects operating in environments
that demand high performance compute
capabilities such as AI and deep learning.
This is because modern data storage
solutions encompass a parallelism that
mimics the human brain and enable multiple
queries or jobs to run simultaneously. They
also deliver uncompromised performance
across all manner of access patterns – small
to large files, random to sequential and low
to high concurrency – all with the ability to
easily scale linearly and non-disruptively,
as the business demands. By building this
type of flash technology into the very foundation of AI projects, it
vastly improves the rate at which AI and ML
initiatives can develop.
TO FEED THE
CENTRAL TO AI.
To cite just one example, Man AHL, a pioneer
in the field of systematic quantitative
investing, leverages Apache Spark on top of
flash storage to create and execute computer
models that make investment decisions.
Roughly 50 quantitative researchers and
more than 60 technologists collaborate to
formulate, develop and drive new investment
models and strategies that can be executed
autonomously. The firm adopted flash
storage to deliver the massive storage
throughput and scalability required to meet
its most demanding simulation applications.
Whether AI is central to your company’s core
competency or not, it is a tool all organisations
should be looking at using to bring efficiency
and accuracy to their data-heavy projects.
Those who don’t could be leaving their business
at a severe competitive disadvantage. n