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.

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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.