Governor Brian Kemp formed a joint initiative on Wednesday between Georgia’s Technical College System, Georgia’s Ministry of Education, Georgia Technology Association, and Amazon Web Services to train 5,500 learners across the state in cloud computing. Announced that it will provide education.

Through this initiative, Georgia high schools and colleges of technology offer cloud computing courses and qualifications tailored to the skills needed to pursue a demanding technical career.

“Georgia’s technology industry is growing rapidly and offers exciting job opportunities for Georgians,” said Kemp. “This collaboration with AWS will give citizens access to innovative training and education to prepare for technical jobs in Georgia.”

The state is working with the AWS Academy program to provide educational institutions with a free, out-of-the-box cloud computing curriculum that prepares students for industry-recognized AWS certifications and high-demand cloud jobs.

Students can also access self-paced online training courses and labs from AWS.

“The use of cloud-based technology is growing rapidly in Georgia, which means there is a growing demand for a skilled workforce in this area,” said TCSG Commissioner Greg Dozier. .. “We are pleased to begin this partnership to provide students and existing information technology workers with the latest training available to ensure success in this growing area.”

Successful software companies manage to deploy releases promptly because they’re great at DevOps. Without DevOps, there will still be a glaring schism between the development and software teams of businesses, making it hard to collaborate and align goals. But with DevOps, both groups work together in all stages of software development to allow for faster fixes, resulting in a better product for the end user.

DevOps practitioners are in great demand, as users expect more from the companies that develop the digital products they use. If you want to become a DevOps engineer, check out the Dynamic 2021 DevOps Certification Training Bundle.

This five-course training package focuses on Git, CompTIA Cloud, Docker, and Ansible, all of which are used in building infrastructure. But first, you’ll start with an in-depth primer on DevOps fundamentals so you can have a full grasp of how it works. Then, you’ll learn how to create repositories in Git, discover Docker technologies such as Docker Hub, Docker Compose and Docker Swarm, and master the open-source tool Ansible to elevate productivity and improve business outcomes. You’ll also get to hone your expertise in preventive maintenance, installation, security, troubleshooting, and networking.

The courses are all self-paced, so you can learn when it’s best for your schedule. Also, the courses are taught by industry insiders with years of experience.

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The courses will introduce you to the fundamentals of Amazon Web Services (AWS), which is a cloud platform that companies use to safely host their infrastructure. The course will walk you through the terminologies, concepts, benefits, and best deployment options for your company. You will also master architectural principles and become a certified AWS solutions architect.

The courses will also provide you with exposure to the cloud platform, which will help you attain valuable expertise in launching, managing, and continuing to operate systems that are completely fault-tolerant. Finally, the course bundle will walk you through Puppet, which is a tool that allows you to automate your IT infrastructure across the entire network. It will walk you through how you can use Puppet for scalability, redundancy, performance, and more.

This deal will provide you with lifetime access that can be used on desktop or mobile devices. Right now, the Amazon Web Services course bundle is on sale for only $59.99.

Microsoft Azure is a leading cloud computing platform in today’s business world, and it looks like it might even become the cloud solution to help the space industry with projects on and off this planet. While time will tell if Microsoft’s competitors attempt to do the same, it’s clear that learning how to work with Azure on earth in 2020 is a helpful set of skills.

You can save hundreds on The Complete Microsoft Azure eBook & Video Course Bundle, which includes six detailed eBooks and four educational video courses that teach you everything you need to know about working with Azure.

This bundle quickly catches you up to speed on everything Azure with six eBooks and more than 15 hours of video instruction. That means you learn Azure’s fundamentals, discover how to work with its advanced features and understand how to help companies, both small and large, make the most of this cloud computing platform. Throughout the bundle, you get guidance on building Azure solutions, learn how to use PowerShell to initiate and execute daily Azure tasks, implement DevOps with Azure and more.

Included materials:

Implementing Azure Solutions [eBook] ($40 value)

Serverless Integration Design Patterns with Azure [eBook] ($40 value)

Azure Networking Cookbook [eBook] ($28 value)

Azure PowerShell Quick Start Guide [eBook] ($24 value)

Migrating Applications to the Cloud with Azure [eBook] ($28)

Mastering Identity & Access Management with Microsoft Azure [eBook] ($48)

DevOps with Azure [Video] ($125 value)

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Cloud computing drives innovation and business around the world. Learning how to work with a leading cloud solution like Azure gives you tangible skills companies need today. Normally $703, The Complete Microsoft Azure eBook & Video Course Bundle is on sale for $30, 95% off its original price tag.

Why VMware Cloud on AWS Requires Serious Training Investment

VMware Cloud on AWS is not simply a new software package — it is an enterprise-grade extension of the VMware stack running on AWS infrastructure. Adopting it means connecting an on-premises data center to a software-defined data center (SDDC), configuring VPN connectivity to secure network traffic, and setting up networking and security components including firewall rules, DNS, and DHCP.

There is no entry-level version of this platform. Admins who are not already confident with ESXi and vCenter will find the environment overwhelming. That said, the platform offers compelling reasons to invest the time: it consolidates and extends data center capabilities, simplifies disaster recovery, and provides a consistent operational model across on-premises and cloud environments.

Pricing is also worth understanding before deployment begins. VMware Cloud on AWS is billed on a per-host, per-hour basis, and costs vary depending on host specifications. Admins need to plan capacity carefully to avoid unexpected spend.

Step 1 — Build the vSphere Foundation First

The recommended starting point is the “VMware vSphere: Install, Configure and Manage” (ICM) course. This course covers the core vSphere skills that underpin everything in VMware Cloud on AWS — because at its heart, the cloud platform is VMware’s core technology running in an AWS-hosted environment.

The ICM course serves two audiences: experienced admins who need to refresh and solidify their knowledge, and those who lack confidence in foundational vSphere concepts. Even though the course focuses primarily on on-premises data center scenarios, the skills it develops translate directly to the cloud context.

VMware offers the ICM course online year-round through its official training portal. The course is a prerequisite — not optional — before moving on to VMware Cloud on AWS-specific material.

Step 2 — Understand vSAN and NSX

Beyond vSphere, two additional technologies are central to VMware Cloud on AWS: vSAN and NSX.

vSAN

vSAN is VMware’s software-defined storage layer. It eliminates the requirement for external shared storage arrays by pooling local disks across ESXi hosts into a distributed datastore. Understanding vSAN is essential because storage is foundational to every workload running in the SDDC, and vSAN does not offer a simplified beginner mode — admins need to understand deployment, policy management, and failure tolerance from the outset.

NSX

NSX is VMware’s network virtualization platform. It allows admins to make network topology and security changes programmatically, without requiring physical intervention on switching or routing hardware. NSX does offer a simplified mode that lets newer admins get started with core networking tasks without being exposed to its full feature set immediately. A more advanced control panel gives experienced admins access to the complete range of configurations.

For admins who are new to both technologies, it is recommended to prioritize vSAN training before NSX, given that vSAN offers less operational flexibility for those who do not fully understand it, while NSX’s simpler mode provides a safer on-ramp for networking tasks.

Step 3 — Complete the ‘VMware Cloud on AWS: Deploy and Manage’ Course

Once admins have solid grounding in vSphere, vSAN, and NSX, they are ready for the “VMware Cloud on AWS: Deploy and Manage” course. This course picks up where the foundational training leaves off and addresses topics specific to the cloud platform, including:

• Key differences between on-premises VMware environments and VMware Cloud on AWS
• How on-premises services integrate with the SDDC
• SDDC deployment and day-two management tasks
• Security best practices for VMware Cloud on AWS, some of which also apply to on-premises environments

This course is available year-round through VMware’s official training portal.

Training Delivery Options and Cost Considerations

The cumulative cost of completing the full training path is significant. However, VMware offers multiple delivery formats — including in-person instructor-led sessions, live online classes, and self-paced options — which gives organizations flexibility in how they structure training for their teams.

Discounts are available through the VMware User Group (VMUG) Advantage program, which can meaningfully reduce per-seat costs for organizations that are already VMUG members. Organizations planning a serious VMware Cloud on AWS adoption should factor training costs into their total investment alongside licensing and infrastructure spend.

The Recommended Training Path at a Glance

1. VMware vSphere: Install, Configure and Manage (ICM) — Foundational vSphere skills covering ESXi, vCenter, and core data center operations.
2. vSAN training — Software-defined storage concepts, deployment, and management.
3. NSX training — Network virtualization fundamentals, with progression from simplified to advanced mode.
4. VMware Cloud on AWS: Deploy and Manage — Platform-specific deployment, integration, and security topics.

Skipping steps in this path is likely to result in operational problems after deployment. VMware Cloud on AWS environments succeed or fail based on how they are managed, and gaps in foundational knowledge have a direct impact on reliability, security, and cost control. The training investment, while substantial, reflects the complexity and capability of the platform itself.

This article is based on reporting originally published on SearchServerVirtualization (TechTarget).

From Grassroots Movement to Enterprise Priority

For much of its early history, DevOps was associated with high-growth technology startups — the so-called “unicorns” — that had the freedom to build culture and tooling from scratch. Traditional enterprises, burdened with legacy systems, regulatory requirements, and large organisational structures, were seen as poor candidates for DevOps adoption.

By 2016, that assumption had been overtaken by reality. Large, complex organisations — banks, retailers, insurers, and government agencies — were not merely following the trail blazed by startups. In many cases they were advancing DevOps practices in ways that smaller companies could not, precisely because the problems they solved were harder: mainframe integration, compliance pipelines, regulated release processes, and multi-team coordination at scale.

What DOES16 Revealed About Enterprise DevOps Maturity

The DevOps Enterprise Summit has always focused on practitioners inside large organisations rather than on vendors or tools. DOES16 continued that tradition, surfacing several themes that reflected the state of enterprise DevOps in late 2016:

• Leadership alignment matters as much as tooling. Presenters consistently pointed to executive sponsorship and cross-functional leadership as prerequisites for sustainable DevOps transformation, not optional extras.
• Value stream mapping went mainstream. Enterprises were using value stream mapping to surface bottlenecks in software delivery — often discovering that the slowest parts of the pipeline had nothing to do with code and everything to do with approval gates and handoffs.
• The “us vs. them” narrative was fading. Development, operations, security, and compliance teams were increasingly framing their work around shared outcomes rather than departmental boundaries.
• Metrics moved beyond deployment frequency. While the four key metrics popularised by research such as the State of DevOps Report (deployment frequency, lead time, mean time to restore, and change failure rate) were widely referenced, mature organisations were beginning to connect delivery metrics to business outcomes.
• Scaling DevOps remained the hardest problem. Organisations that had succeeded in one team or one product line were wrestling with how to replicate that success across hundreds of teams without mandating a single prescriptive process.

Key Trends Shaping Enterprise DevOps in 2016 and Beyond

Continuous Delivery at Enterprise Scale

Continuous delivery pipelines were no longer the exclusive domain of cloud-native startups. Enterprises were investing heavily in pipeline automation, with release orchestration emerging as a distinct discipline — coordinating deployments across dozens of interdependent services, environments, and teams in a controlled and auditable way.

Cultural Transformation as a Technical Problem

One of the more striking shifts visible at DOES16 was the willingness of enterprise speakers to discuss organisational change in concrete, engineering terms: reducing batch sizes, shortening feedback loops, making work visible. Cultural change was increasingly being treated not as a soft, aspirational goal but as a system design challenge with measurable outputs.

Security and Compliance Integration

DevSecOps — integrating security practices directly into the delivery pipeline — was gaining traction. Rather than treating security review as a late-stage gate, leading enterprises were embedding automated security scanning, policy as code, and compliance checks earlier in the pipeline to catch issues without slowing delivery.

The Role of Release Orchestration

Electric Cloud, the publisher of the original article, were advocates of release orchestration as a practice distinct from simple CI/CD automation. For enterprises managing complex, multi-team deployments, orchestrating releases across heterogeneous environments — including mainframes, on-premises infrastructure, and cloud — was a genuine operational challenge that point-to-point pipeline tools did not fully address.

Why Enterprise DevOps Progress Matters

The significance of large enterprises leading DevOps innovation extends beyond any individual organisation. Enterprises operate at a scale and under constraints — regulatory, financial, reputational — that startups do not. When a major bank or a healthcare provider successfully implements continuous delivery, it demonstrates that DevOps is not a niche practice for companies that can afford to move fast and break things. It proves that disciplined, safe, and rapid software delivery is achievable inside some of the most constrained operating environments in the world.

That proof point accelerates adoption across entire industries and changes the conversation with regulators and auditors who had previously viewed agile delivery as incompatible with governance requirements.

Original Source

This article summarises themes from the Electric Cloud blog post published in November 2016: DOES16 San Francisco: The Future of DevOps in the Enterprise – Trends & Predictions. Electric Cloud (now part of CloudBees) specialised in release automation and orchestration for enterprise software delivery.

Over the past decade, “continuousness” has become one of the defining characteristics of modern software engineering. Driven by the broader industry adoption of Agile methodologies and DevOps culture, practices like Continuous Integration (CI), Continuous Delivery (CD), and Continuous Deployment have moved from niche to mainstream — particularly in SaaS product development and web application delivery.

Despite how frequently these terms appear together, they describe distinct stages of a software pipeline. Understanding the differences — and how they interconnect — is essential for any engineering team looking to reduce release risk, shorten feedback loops, and ship software with confidence.

What Is Continuous Integration (CI)?

Continuous Integration is the practice of merging developer code changes into a shared repository frequently — often multiple times per day. Each merge triggers an automated build and test sequence, giving the team near-immediate feedback on whether the new code integrates cleanly with the existing codebase.

The core goals of CI are to:

• Catch integration bugs early, before they compound.
• Reduce the pain of merging large, long-lived feature branches.
• Give every developer a consistent, verified baseline to work from.
• Produce a tested build artifact that downstream stages can rely on.

A typical CI pipeline includes steps such as compiling the application, running unit and integration tests, performing static code analysis, and generating a build artifact (e.g., a Docker image or compiled binary).

What Is Continuous Delivery (CD)?

Continuous Delivery extends CI by ensuring that the verified build artifact is always in a deployable state. In a Continuous Delivery workflow, every successful build is automatically tested further — through integration, acceptance, performance, and/or end-to-end tests — and then staged for release to production.

The key distinction: a human still approves the final push to production. Continuous Delivery guarantees readiness; it does not mandate automation of the final release step. This makes it particularly valuable in regulated industries or teams that require a sign-off or change-management process before production deployments.

Continuous Delivery pipelines typically include:

• Automated deployment to staging or pre-production environments.
• Smoke tests and acceptance tests run against the deployed environment.
• Artifact versioning and release notes generation.
• A one-click (or API-triggered) mechanism to promote a release to production.

What Is Continuous Deployment?

Continuous Deployment takes the pipeline one step further: every build that passes all automated tests is deployed to production automatically, without any manual approval. This is the most aggressive form of continuousness and requires a high degree of confidence in the test suite, observability stack, and rollback capabilities.

Teams practicing Continuous Deployment commonly rely on:

• Feature flags to decouple deployment from feature release.
• Blue/green or canary deployments to limit blast radius.
• Robust monitoring and alerting to detect regressions within minutes of a release.
• Automated rollback triggered by error-rate thresholds or failed health checks.

Not every team or application is a good fit for Continuous Deployment. High-compliance environments, hardware-adjacent software, or applications with complex data migrations may be better served by Continuous Delivery with a controlled release gate.

How the Three Practices Relate

It helps to think of CI, Continuous Delivery, and Continuous Deployment as a progressive maturity model:

Each layer depends on the one before it. You cannot have a reliable Continuous Deployment workflow without solid CI foundations. Similarly, Continuous Delivery is only trustworthy if the CI stage already produces a well-tested artifact.

Unique Challenges for Web and CMS-Based Projects

While CI/CD concepts originated in application software development, they apply equally — with some added complexity — to web development and content-managed sites. Websites must manage not just code and configuration, but also content state, database schema migrations, and media assets. This creates challenges that a purely code-centric pipeline does not face:

• Database migrations must be versioned and applied in sync with code deployments to avoid schema mismatches.
• Content synchronization between environments (production, staging, development) requires deliberate strategies to avoid overwriting live content with stale test data.
• Third-party integrations such as CDNs, payment gateways, and marketing tools may behave differently across environments.
• Cache invalidation must be coordinated with deployments so users see updated content immediately.

Platforms designed with these workflows in mind — including managed hosting providers with built-in environment promotion tools — can reduce much of this complexity. However, the underlying CI/CD principles remain the same regardless of the platform.

Getting Started: Key Principles

If your team is beginning its CI/CD journey, a few foundational principles will serve you well:

1. Automate the build first. Before anything else, ensure every commit triggers a clean, reproducible build. Flaky or manual builds undermine the entire pipeline.
2. Write tests that you trust. CI is only as valuable as its test coverage. Invest in tests that reflect real failure modes, not just happy-path scenarios.
3. Keep the pipeline fast. A pipeline that takes 45 minutes to run discourages frequent commits. Aim for fast feedback, using parallelism and test prioritization where possible.
4. Make deployment boring. The goal is for every deployment to be a non-event. Frequent, small releases are safer than large, infrequent ones.
5. Instrument everything. Monitoring, logging, and alerting are not optional in a CD environment — they are the safety net that makes automation safe.

Further Reading

For a deeper exploration of how these principles apply specifically to web platforms and content-managed sites, the original discussion at Pantheon’s engineering blog provides useful context from a managed hosting perspective.

Originally reported by IT Business Edge (November 2016).

Overview

In late 2016, CA Technologies made a notable push to broaden its DevOps strategy beyond traditional distributed systems to explicitly include mainframe environments. A key element of this effort was the introduction of machine learning algorithms designed to predict performance issues in mainframe applications before they manifest as outages or degradations — a meaningful step forward for enterprises that depend on mainframe infrastructure for mission-critical workloads.

Why Mainframe DevOps Matters

Many large enterprises — particularly in banking, insurance, and government — continue to run core business logic on mainframe systems. For years, these environments were largely excluded from the DevOps conversation, which tended to focus on cloud-native or distributed architectures. CA Technologies recognized this gap and positioned itself as a vendor capable of delivering DevOps practices consistently across both worlds.

Bridging distributed and mainframe DevOps matters for several reasons:

• Unified pipeline visibility: Development and operations teams need a single view of application health regardless of where workloads run.
• Consistent release velocity: Slow, manual mainframe release processes create bottlenecks when the rest of the delivery pipeline has been automated.
• Risk reduction: Mainframe outages are often high-impact; predictive tooling can help teams intervene before users are affected.

Machine Learning for Predictive Mainframe Operations

The most technically significant element of CA Technologies’ announcement at the time was the application of machine learning to mainframe performance monitoring. Rather than relying solely on static thresholds or reactive alerting, machine learning models can be trained on historical performance data to identify patterns that precede degradation events. When those patterns re-emerge, the system can alert operators — or trigger automated remediation — before end users experience an impact.

This approach aligns with a broader industry trend toward AIOps (Artificial Intelligence for IT Operations), where analytics and automation reduce the manual investigation burden on operations teams. Applying this to the mainframe was a notable differentiator for CA Technologies in 2016, as most AIOps tooling at the time was focused on cloud and distributed environments.

CA Technologies’ Broader DevOps Portfolio

CA Technologies (later acquired by Broadcom in 2018) offered a broad set of DevOps-related products covering areas including:

• Continuous delivery and release automation: Tools to orchestrate and automate application deployments across environments.
• Agile planning: Portfolio and project management capabilities aligned with Agile and Lean principles.
• Test management and service virtualization: Enabling teams to test earlier and more frequently, including in environments where downstream dependencies are unavailable.
• Application performance management: Monitoring and analytics across distributed and mainframe applications.
• Mainframe DevOps: Source code management, change management, and now predictive analytics specifically for z/OS and related platforms.

Industry Context

In 2016, DevOps adoption was accelerating rapidly across software-forward companies, but many traditional enterprises faced significant challenges adapting DevOps principles to legacy infrastructure. CA Technologies’ focus on mainframe DevOps addressed a real and underserved need: helping organizations that could not simply migrate away from mainframes to still participate in the speed and reliability benefits that DevOps practices offer.

This move also reflected a competitive dynamic in the enterprise tooling market, where vendors like IBM, Compuware, and CA Technologies were each working to claim leadership in mainframe modernization without requiring wholesale platform replacement.

Key Takeaways

• CA Technologies extended its DevOps tooling to cover mainframe environments alongside distributed systems.
• The introduction of machine learning for predictive mainframe performance monitoring was a technically forward-looking step for 2016.
• Enterprises dependent on mainframe infrastructure gained a clearer path to DevOps adoption without requiring platform migration.
• CA Technologies was subsequently acquired by Broadcom in 2018; many of its DevOps products continue under the Broadcom portfolio.

For the original reporting, see the IT Business Edge article.