Industry executives and experts share their predictions for 2019. Read them in this 11th annual VMblog.com series exclusive.
Contributed by Ned Bellavance is the Director of Cloud Solutions at Anexinet
2019: The Year of the Cloud Native Enterprise, Hybrid Cloud Round Two, and Applied Machine Learning
Digital transformation
strategies take many forms and embrace many technologies. Thankfully, so does
Anexinet. Here are our predictions-and prescriptions-actual initiatives you
should be working on in the coming year. Don't start-off the new year
unprepared. Learn how to best position your business for success with our 2019
Predictions and Prescriptions.
1. Cloud native is
coming to the enterprise. The mass migration to the cloud is
continuing. I've seen a steady uptick in the number of organizations that
are looking to migrate their workloads to the cloud. The primary
migration technique? Lift & shift. It's simple, relatively
speaking, and doesn't require significant changes to the way you operate your
IT infrastructure. However, once you find yourself in the public cloud,
you may quickly realize that simply moving your workload is not going to
deliver on the cloud promise of resiliency, elasticity, and cost
efficiency. If you want to take full advantage of all the cloud has to
offer, it's time to do the harder work of transforming your workloads and
organization to be more cloud native. That requires both refactoring your
applications, and adopting DevOps principles. Look for a steady rise in
both of those trends.
2. Hybrid cloud is
back for round two. There was a theory of hybrid cloud where
organizations would develop workloads that spanned both private and public
clouds. That dream failed to materialize, since traditional applications
are not set up in a way that can handle the bifurcated nature of hybrid cloud,
nor the necessary decoupling of services within the application to support such
a separated scenario. Now that applications are becoming more
cloud-native, there is an opportunity to design them in such a way that will
support the hybrid cloud paradigm. Certain components of the workload may
perform better on-premises, while other components are better suited for the
cloud. A great example would be machine learning software that trains
models in the cloud, but performs inference on-premises. The launch of
solutions like Microsoft Azure Stack and AWS Outposts makes it possible to
develop these applications using a consistent environment across both public
and private cloud, and provide operators with a consistent environment to
manage and monitor.
3. 2019 will be the
year of applied Machine Learning. AI and ML hit peak hype cycle in
2017, where you couldn't read a product announcement without finding one of
those two phrases embedded. Anything from a next-gen firewall to my WiFi
crockpot somehow suddenly had the power of AI or ML! Now that the marketing
hype has cooled a bit, the serious work of harnessing the power of machine
learning is settling in. AWS devoted almost a full hour to ML in their
re:Invent keynote, and they have opened up their entire ML learning catalog to
the world. Machine learning is no longer the province of academia and
data scientists. Public cloud vendors like AWS, Azure, and Google Cloud
have made the technology easily available to anyone, with a low barrier of
entry. New tools, interfaces, and languages are making ML more
approachable and understandable to some who isn't working on their
post-doc. 2019 will see an explosion of ML innovation and application in
areas that we would have never considered.
##
About the Author
Ned Bellavance is the Director of Cloud Solutions at
Anexinet, specializing in Enterprise Architecture both on-premise and in
the cloud. Ned holds a number of industry certifications from
Microsoft (as a Microsoft MVP), VMware, Citrix, and Cisco. He also has a
B.S. in Computer Science and an MBA with an Information Technology
concentration. With over 15 years of experience in IT, Ned started as a humble helpdesk
operator and worked up through the ranks of systems administration and
infrastructure architecture, developing an expansive understanding of IT
infrastructure and the applications it supports in the process.