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Usermind 2018 Predictions: CIOs must lead in climbing the learning curve of container orchestration & secure cloud deployment

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual VMblog.com series exclusive.

Contributed by David Talby, CTO, Usermind

CIOs must lead in climbing the learning curve of container orchestration & secure cloud deployment

With the mass adoption of Kubernetes and the continued proliferation of microservices, 2018 will be the year engineering teams focus on how to best deploy, secure and operate containerized apps. Specifically, they'll need to ensure that monitoring, backups, security and network controls are built properly -- and this may take more effort than expected. Here are the four biggest challenges associated with cloud deployment in 2018:

Specialized talent. As with all new technologies, there are very few people with the niche training and expertise to build, configure, and scale these complex cloud environments. Companies must maximize their engineering resources by splitting work into three buckets. First, some ‘keep the lights on' tasks can be automated or be done consistently by existing talent. Second, new core technologies need the attention of your top people so that they get hands-on experience and you gain real in-house expertise. Third, new complex tasks can be outsourced to specialist third parties, as long as some knowledge transfer is involved so that you get the speedy results now without the lock-in later.

Tightened Security. Even the more advanced container technology such as Docker and Kubernetes are not inherently secure. Security controls are required at multiple levels - network isolation, storage encryption, key management, role based access, audit logs and many others. Additionally, regulated industries like healthcare, education and finance will require stricter compliance. Security, privacy and compliance must be baked into the architecture from the ground up. Point solutions and "quick fixes" don't work. Controls also have to consider the entire technology stack since vulnerabilities often exist where multiple systems were designed separately with different security assumptions. Even if the API connects, the security protocol may not translate.

More container services. Engineering shops will leverage the managed container services that all major cloud providers now offer, even though that may increase lock-in to cloud providers. Consider carefully the benefits of using a cloud provider's managed container service, versus rolling a Kerberos deployment. Since Docker and Kerberos will become core technologies for which you'll have good in-house knowledge, and since there are reusable, standard scripts to deploy them, this is an opportunity to be (almost) completely cloud-agnostic.

Machine Learning Matures. Businesses want to use machine learning, deep learning and other AI to act on insights gathered from analyzing and modeling their customers' and systems' behavior. However, most enterprises will still struggle with core issues such as siloed data, data quality, ill defined business goals for ML and lack of a data science platform - all of which undermine the investment. Companies must understand their current level of maturity in data science and plan to address all the missing elements required to launch and monetize machine learning. Automated learning in production will not happen before data integration, data quality, measurement pipelines and an analytics platform are in place.

There's a tremendous opportunity for enterprises to capitalize on technological developments and investments in 2018, and success will be measured in the context of execution - how quickly can your team learn and make this happen?

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About the Author

David Talby 

As CTO of Usermind, David leads the architecture, design, and delivery of the Usermind platform. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft's Bing group, where he led business operations for Bing Shopping in the US and Europe, and at Amazon, where he built and ran distributed teams that helped scale Amazon's financial systems. David holds a PhD in computer science and master's degrees in both computer science and business administration.

Published Thursday, November 16, 2017 7:33 AM by David Marshall
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