Page 27 - Logistics News - May June 2022

CL O UD C O MPUTIN G

Warehousing leaders need to be aware that if they want is done simply because most of the networking tools needed
to embrace data science and AI, they need to take the first are made available right in your cloud project. The cloud
step of embracing the cloud for data services. removes the need for vendors to spend valuable time getting
up to speed with on-premise, sometimes in-house built,
Database technologies infrastructure.
The top three cloud providers (Azure, AWS and GCP) all
specialise in state-of-the-art database technologies that Given the hype around cloud technologies, why
modern ML workflows rely on for fast processing of large wouldn’t every supply chain leader push their departments
amounts of data. Utilising the cloud offers organisations into the cloud? Top concerns we hear from our customers
access to this infrastructure and this has democratised, to centre around data security and preventing unauthorised
some degree, the use of AI. access to their data. Leaders fear that if they move their
data into the cloud, they’re leaving themselves exposed
On-premise solutions do exist, but they can be very to cyber and ransomware attacks that could cripple their
costly if you don’t have the tech talent to support in-house businesses.
infrastructure. Talent shortages can add even more risk to
an in-house solution in cases when your primary tech lead The reality is that cloud providers have been providing
leaves for a new job and you struggle to replace the talent. data services to health care companies and governments
with highly sensitive data for years with varying levels of
During times of scaling up workflows, the cloud can desired security. If you require data security, every cloud
handle large datasets and you won’t need to purchase provider has the ability to offer the level of security you
additional hardware for maintenance. The buying and need, with teams of experts who are fully dedicated to
managing of equipment are done by the cloud service providing cutting-edge security compared to in-house IT
provider. This will save you in upfront capital costs and also teams.
ongoing hardware maintenance costs.
Embracing transformation
Centralised data In summary, we’ve explored some of the key reasons why
Data silos are one of the most painful problems to work the cloud is important to AI and ML adoption and even
through as a data scientist and can lead to projects coming some of the reasons why people are hesitant to move
in over budget and late. As a data scientist, nothing makes into the cloud. In short, AI and ML have the potential
me happier than working with a customer who has taken to transform warehouse operations for very little cost
the time to centralise data infrastructure because I know I’ll compared to alternatives.
be able to get access to required data without issues. When
data is siloed throughout an organisation, it can be difficult, Cloud works on the principle of ‘pay for what you need’.
if not impossible, to get the data required for a particular The cloud’s pay-per-use model is good for companies who
project. The cloud offers several solutions to help companies wish to leverage ML capabilities without much expenditure.
centralise their data and maintain security and permissions.
The really exciting thing about the cloud is the way that
Engineering set-up and integrations make up a large it allows for collaboration. The more data in the system, the
portion of the cost of purchasing vendor AI technology. better the intelligence and learnings that can be gleaned
You will need to provide specific hardware and operating from it. Data-rich, real time, constantly enhanced and
systems, and install required software and packages in order optimised. With AI and ML insights powered by the cloud,
to conform to the needs of each vendor. This can cause the enhancements to your core processes and procedures
several headaches and slow down implementations, which can be transformational.
will drive up costs. L O GI S T I CS NEWS
If business leaders are serious about adopting AI within
When both parties are already working in the cloud, their warehouses, they will need to get serious about cloud
there is a clear path to production for those AI services. This adoption too. •

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