As a proud member of Atlanta Technology Professionals (ATP), a dedicated disability advocate, and the founder of newly launched 501(c)(3) Disability Scholarship to support disabled college students and Science scholarship to advance STEM, I am excited to celebrate Disability Pride Month with you. This month is a testament to the strength, resilience, and creativity that disabled individuals bring to our tech community and beyond.

In the dynamic world of technology, the contributions of disabled professionals are often underappreciated. Yet, it is the third largest market segment, representing unique perspectives and problem-solving skills that drive true innovation and inclusivity. Disability Pride Month offers an intentional opportunity to recognize these contributions and to promote an environment where everyone can flourish.

Launching the Disability and STEM scholarships have been a deeply personal and rewarding journey. Inspired by my own engineering background and challenges navigating higher education as a disabled student, these scholarships aim to eliminate barriers and provide opportunities for future tech leaders. Supporting disabled students is not only an investment in their future but also an enrichment of the tech industry with diverse talents and perspectives.

In addition to this, I am thrilled to announce my upcoming book, “Inclusive Innovation: Harnessing AI, Robotics, and Machine Learning for Disability Tech.” This book explores how emerging technologies can revolutionize the lives of disabled individuals, creating more opportunities for independence and participation. It highlights the critical need for our industry to prioritize inclusivity and equity in technological advancements.

As we mark the 34th anniversary of the Americans with Disabilities Act (ADA) on July 26th, we reflect on the progress made in disability rights and the work still ahead. The ADA was a landmark moment in the fight for disability justice, providing a legal framework to protect the rights of disabled individuals. Yet, achieving true equity requires ongoing advocacy and action.

At ATP, we have a unique platform to champion inclusivity through allyship and awareness. Our commitment to diversity, equity, and inclusion must go beyond rhetoric to concrete initiatives that drive meaningful change. This means ensuring our workplaces are accessible, our hiring practices are inclusive, and our professional development programs intentionally support the growth of disabled employees.

Disability Pride Month is also a time to honor the principles of disability justice, which calls for recognizing the intersectionality of disabled people’s experiences and advocating for systemic changes that uplift our collective community. It is about creating spaces where disabled voices are heard, valued, and empowered to lead.

As we celebrate this month, let us remember that true inclusion requires continuous effort. It is about recognizing the value of every individual, advocating for necessary accommodations, fostering, and cultivating a culture of belonging. Together, we can dismantle barriers and build a tech community where everyone, regardless of their abilities, neurodivergence, and differences, can succeed.

I encourage all members of ATP to engage with and support disabled colleagues, learn about disability rights, and participate in initiatives that promote accessibility and inclusion. By doing so, we not only honor the spirit of Disability Pride Month but also pave the way for a more equitable and innovative future.

Thank you for joining me in all you do to celebrate Disability Pride Month and for your commitment to making Atlanta’s tech community a place where everyone can thrive. Together, we can make a difference and continue to lead the way in creating a truly inclusive industry.

DevOps is a smart way to integrate software development and IT operations, hence the term. By using automated tools and collaboration, DevOps speeds up software delivery, improves quality, and ensures reliability. Think of it as a hybrid approach that streamlines workflows by making them smoother and faster. With DevOps, companies can innovate quicker, reduce downtime, and adapt easily to changes, making it essential for today’s tech-driven businesses. With DevOps, the complicated and time-consuming model of giving and receiving feedback to and from the development and operations team is now redundant. Advancements in this field allow for a streamlined flow of communication, as shown by its symbol, the infinity sign. This technology makes developing, testing, and releasing software more reliable and accelerated.

Keep reading to learn how DevOps evolved into what it is today, its current trends, its best practices, and the future outlook of DevOps.

DevOps Evolution

The evolution of DevOps began in the early 2000s and continues to the present and beyond. Let’s examine the key milestones in DevOps evolution and how it became what it is today.

The Agile Movement

The emergence of Agile methodology, based on five steps: define, design, build, test, and release, came to the forefront in the early 2000s. The need for much better collaboration between development and operations teams was realized during this time. A tech writer from a LinkedIn article states, “Agile set the stage for DevOps by emphasizing rapid development cycles and customer-centricity.”

Continuous Integration (CI)

CI became popular in the mid-2000s as developers regularly merged code into a shared repository, automating builds and tests to reduce integration problems and detect and fix bugs faster.

Continuous Deployment (CD)

Building on the success of CI, Continuous Deployment emerged in the early 2010s, automating the release of software changes to production to swiftly and reliably deliver code changes, further speeding up the development cycle.

Infrastructure as Code (IaC)

IaC took off in the early 2010s, transforming infrastructure management by letting teams define and control infrastructure with code. This streamlined processes, cut errors, and made infrastructure scalable and repeatable.

Microservices

Microservices gained traction in the early 2010s, splitting applications into smaller, independent services for easier development, deployment, and scaling, aligning with DevOps principles.

• Containerization

Docker’s 2013 release popularized containerization, allowing developers to package applications and dependencies into containers for consistent environments, smoother deployments, and scaling.

DevSecOps

DevSecOps emerged circa 2017, integrating security throughout the DevOps cycle to bolster application security from planning through deployment.

These key practices and innovations have shaped the evolution of DevOps, driving improved collaboration, efficiency, and reliability in software development and operations. Fast-forward to today, major companies like Netflix, Amazon, Etsy, Target, Facebook, and Adobe utilize DevOps to keep their businesses running smoothly.

If you are more of an auditory/visual learner, there is a quick video about the history of DevOps with a twist.

Current Trends

DevOps continues to grow in 2024 as automation, security, and scalability advancements grow. Key trends such as AI integration, the cloud, microservices, GitOps, and DevSecOps are reshaping how development and operations teams collaborate, ensuring faster and more secure software delivery. In our ever-changing technology landscape, these types of innovations are what we need to continue pushing forward.

AI and DevOps

We all know that AI is permeating the world of technology, and tech gurus have found ways to integrate it to make our lives and jobs easier. Combining AI with DevOps is pushing businesses toward greater efficiency and scalability.

In an article from LinkedIn on how AI is transforming DevOps, the author lists ten benefits of integrating AI with DevOps.

• Enhanced automation
• Predictive analysis
• Continuous integration and delivery
• Intelligent resource management
• Enhanced security
• Intelligent incident response
• Data-driven decision making
• Scalable infrastructure management
• Facilitated collaboration
• Continuous learning and improvement

The author states, “By leveraging AI-driven automation, analytics, and decision-making capabilities, organizations can enhance efficiency, scalability, and reliability across the software delivery lifecycle.”

Companies across the globe are embracing AI and how it can join DevOps to revolutionize the tech industry.

The Cloud and DevOps

Another trending technology that organizations and businesses leverage the power of is the cloud. When DevOps joins forces with the cloud, it creates a more dynamic tech solution.

By incorporating cloud-native technologies, DevOps practices become more agile, resilient, and scalable, ultimately leading to faster innovation and a more responsive development process.

According to tech experts at Aspire Systems, “Reports have claimed that over 32% of organizations were using the Cloud-Native approach before the onset of the pandemic, with the number rising to about 55% the following year. With the power of Containerization, Cloud-Native technology has the potential to decrease the stress on infrastructure and minimize security risks.”

Using this type of technology has certainly revolutionized the way businesses do business. Now, blend the power of Cloud-Native with DevOps, and they have a well-oiled machine with undeniable benefits. They have a symbiotic relationship, helping each other create exactly what we need. The author with Aspire Systems shares just exactly how they benefit each other:

“DevOps leverages the flexibility and scalability of cloud computing to automate the software development and deployment process, enabling businesses to deliver high-quality applications at a faster pace. At the same time, DevOps has helped cloud computing evolve, with its focus on automation and collaboration driving the development of new cloud-based tools and services.”

Overall, the combination fosters a synergistic relationship where both technologies enhance each other, leading to a more streamlined and powerful tech solution.

Microservices and DevOps

Microservices are particularly well-suited for large and complex applications where agility, scalability, and maintainability are important. They are typically used with tools like Docker and Kubernetes to manage services efficiently. Simply put, they break an application into multiple “micro” services. Microservices ensures that if there is a major issue with one part of the application, it won’t affect others.

In an article by BMC, the author points out that DevOps and microservices are “tailor-made” for each other. She states, “The role microservices play in DevOps includes streamlining the DevOps process and increasing productivity and quality of the application while moving developments to a flexible architecture. This leads to developing cloud-native applications capable of fulfilling any user demand.”

This example shows how the synergy of DevOps and microservices enables numerous user benefits.

Best Practices

Follow these four quick tips to utilize DevOps in the best way possible for your business:

• Foster a collaborative culture and reach for common goals.
• Constantly seek feedback to optimize DevOps workflows.
• Automate processes to minimize errors and speed up delivery time. Continuously monitoring with automated testing is key.
• Ensure you are utilizing the correct tools that integrate perfectly with DevOps; if you have an expert team, this shouldn’t be a problem.

By following these best practices, organizations can enhance their DevOps processes, leading to more efficient, secure, and scalable software development and operations.

Future Outlook

DevOps isn’t going anywhere—it is only predicted to skyrocket and become more commonplace as companies understand its advantages, especially in the United States. Predictions for the future of DevOps surround increased automation, collaboration, an emphasis on security, and hybrid-platform approaches.

A report by Spacelift highlighted that “The DevOps market is forecast to grow at a CAGR of 19.7% from an estimated $10.4 billion in 2023 to $25.5 billion in 2028. North America is the largest DevOps market, with 38.5% of the global market in 2023. 61.21% (132,180) of companies that use DevOps services technology software are from the United States.

The shining future of DevOps is creating new, highly demanded careers, such as engineers, consultants, and trainers.

With new or quickly advancing technology, there are always challenges. Being proactive and forward-thinking about these challenges will allow companies to be on their game when issues arise in the future.

These are a few challenges and organizational needs to consider with the future of DevOps:

• Finding trained, top-tier DevOps professionals to work with may be difficult at first. However, as DevOps gains traction, the industry will have more of an abundance of marketable DevOps experts.
• Personnel may be resistant to change. DevOps is a mindset, a culture if you will. But just like with any technological advancement, individuals need to be trained and feel powerful with the knowledge they’ve learned to operate the new tool effectively.
• Data management is another DevOps challenge. A 2022 GitLab survey revealed that numerous teams face challenges accessing and managing data, and some organizations fail to track essential information. Organizations must adopt appropriate tools and processes to overcome these issues and ensure data is accessible and well-managed.
• Cybersecurity is also a challenge to consider. Organizations must prioritize security and know how to integrate security protocols into DevOps processes appropriately.

With any innovation comes curiosity, an understanding of challenges, and excitement about the future. DevOps is one you can look forward to getting to know for the long run.

Conclusion

The outlook of DevOps in the world of technology is nothing short of revolutionary. With its emphasis on automation, collaboration, and continuous improvement, DevOps transforms how organizations develop, deploy, and manage software. DevOps’ efficiency, scalability, and innovation potential are set to soar as we integrate more advanced cloud-native technologies and embrace microservices. The future is bright, and DevOps is at the forefront, driving the next wave of technological advancement.

Edge computing is revolutionizing the way data is processed. It is like bringing the power of a big, central computer closer to where the action is happening, right at the edge of the network. Imagine you have a smart device, like a security camera, that needs to analyze video footage quickly. Instead of sending all the video data to a distant data center, the camera or a nearby device does the processing. And IoT devices have shortcomings—-there is simply too much data coming and going. The volume is overwhelming, and more expensive and expansive connections to clouds and data centers are needed. 

Distant data processors aren’t always reliable, especially in a time of need. When processing power is placed “on the edge” or closer to the devices, the benefits of edging are seen. This means less latency and downtime, faster connections, and less device damage. Edge computing does not mean that long-term data storage is now meaningless—they work together. 

Read on to learn more about the history behind edge computing, the benefits and applications, and AI’s role in its presence. 

Edge Computing Explained

What is edge computing?

Data processing has become more complex with the rise of AI and the cloud. Edge computing aims to help overwhelmed networks and infrastructure process data more quickly and efficiently. In layperson’s terms, edge computing is computing as close to a data source as possible, which has multiple advantages; therefore, many companies are saying goodbye (sort of) to distant data centers and hello to on-site processing.  

Why is it important?

Getting data to a distant data center, which can take time and use a lot of internet bandwidth, can cause issues depending on the need for the data. Going back to the security camera example, a house owner without an alarm system needs to know ASAP if there is an intruder or suspicious event on their property while they are away. If the data are latent, they might need more time to call the police. There are even more life-threatening scenarios when edge computing will be beneficial, such as aviation safety, emergency response during a natural disaster, or receiving a serious medical diagnosis. 

Aside from these grave scenarios, edge computing has advantages in office settings, corporations, hospitals, etc. According to an IBM article on the topic, the author writes:

“Edge computing offers a more efficient alternative; data is processed and analyzed closer to the point where it’s created. Because data does not traverse over a network to a cloud or data center to be processed, latency is reduced. Edge computing—and mobile edge computing on 5G networks—enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.”

Some other noted benefits include cost efficiency, better data security and privacy protections, and overall reliability. 

What is the history of edge computing? 

• Before the 1970s, when big tech companies such as Apple and Microsoft began thriving, computing was basic, and computers took up entire rooms. Individuals soon owned personal computers at home and in the office, and not too long after, the World Wide Web was invented. After that, things swiftly began to change, eventually leading to the rise of edge computing. 
• 1980s: Client-server architecture became prevalent, marking the beginning of moving computation closer to the data source by distributing tasks between clients and servers.
• 1990s: Content Delivery Networks (CDNs) were developed to reduce latency by caching content closer to users. Akamai Technologies, founded in 1998, became a prominent player in this field.
• 2000s: Mobile computing gains traction with the rise of smartphones, necessitating efficient, localized data processing. The popularization of “cloud computing” emphasized centralized data processing, but its latency and bandwidth limitations led to the exploration of edge solutions. Later, Cisco introduced “Fog Computing” to emphasize the need for decentralized infrastructure to handle the growing data from IoT devices.
• 2010s: In the early 2010s, the Internet of Things (IoT) growth increased the need for edge computing to manage data locally, reducing latency and bandwidth usage. In 2012, the European Telecommunications Standards Institute (ETSI) introduced the concept of Mobile Edge Computing (MEC) to bring cloud capabilities to the edge of mobile networks. By the mid-2010s, major tech companies like Amazon, Microsoft, and Google began offering edge computing services and solutions. In 2016, the OpenFog Consortium was established to develop standards and architectures for fog and edge computing.
• 2020s: The COVID-19 pandemic accelerated digital transformation and the adoption of edge computing to support remote work, online learning, and telehealth. Soon after, the integration of edge computing with 5G networks enhanced its capabilities, enabling faster and more reliable data processing closer to users. The OpenFog Consortium merges with the Industrial Internet Consortium (IIC) to form the IIC, continuing the push for edge computing standards and innovation.

Edge computing continues to evolve with technological advancements, especially AI. We can only imagine what heights it will soar to within the next decade. 

The applications of edge computing

According to the research, the global market for edge computing is projected to reach USD 116.5 billion by 2030, expanding at a compound annual growth rate (CAGR) of 12.46% from 2022 to 2030. Edge computing provides realistic and effective applications across various industries, and it’s not going anywhere. 

An article from Xenonstack lists six industry-specific applications that ultimately make work in these areas run more smoothly and securely. 

• The banking and finance industry uses edging computing for ATM security and data privacy. 
• The manufacturing industry uses it for condition-based monitoring and predictive maintenance. 
• The retail industry utilizes edge computing for big data analytics and inventory management. 
• The automobile industry uses it for driver assist, predictive maintenance, and driver monitoring. 
• The healthcare industry uses edge computing to ensure higher patient care and overall health and safety. 
• The agricultural sector utilizes it to monitor soil quality, animal health, and crop analysis. 

While this isn’t a comprehensive list of edge computing applications, these are clear examples of how they can help an industry be more productive and efficient. 

Edge computing’s role in data processing 

Edge computing plays a significant role by bringing computation and data storage closer to where it is needed, rather than relying solely on a centralized data-processing warehouse or cloud. Here are some key roles it plays:

Reduced Latency

Edge computing reduces latency by processing data closer to its source, crucial for real-time applications like autonomous vehicles, industrial automation, and augmented reality.

Bandwidth Optimization

Edge computing reduces bandwidth usage and costs by locally processing data, minimizing the need for transmission to central servers or the cloud.

Improved Reliability

Edge computing enhances system reliability and continuous operation in critical applications by allowing local data processing even without cloud connectivity, which is crucial in healthcare industries.

Enhanced Security and Privacy 

Edge computing enhances security and privacy by processing sensitive data locally, reducing transmission risks, and aiding compliance with privacy regulations.

Scalability

Edge computing efficiently handles large IoT data volumes by distributing the processing load across multiple devices, allowing scalable solutions.

Contextual Processing

Edge computing enables immediate, localized decisions in context-aware applications like smart homes, predictive maintenance, and environmental monitoring.

Cost Efficiency

Edge computing reduces the need for costly centralized infrastructure and lowers operational expenses, making it economically beneficial.

Support for AI and Machine Learning

Edge computing devices run local AI models, which are crucial in real-time decision-making in facial recognition, predictive analytics, and anomaly detection.

It is clear that edge computing benefits individual companies, organizations, and entire industries. 

Are you interested in learning how the tech company Verizon uses edge computing? Their site promotes several real-world case studies that provide interactive learning with specifics, diagrams, and more. 

What is the role of AI in edge computing? 

Artificial Intelligence continues to experience exponential growth, leading to innovations and discoveries that allow for new, beneficial applications across various industries. Edge computing and AI are like synchronized dancers, seamlessly coordinating their movements to deliver precise performances in real time. 

In an article by IBM, the author describes AI on the edge: “Simply stated, edge AI, or “AI on the edge,” refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection.”

Edge computing is monumental on its own, and adding AI will take it to the next level. 

In a piece by CIO, the author touts Dell as innovative and forward-thinking regarding AI and edge computing. Dell’s NativeEdge, edge servers, gateways, and storage solutions each incorporate AI on the edge. 

Cisco Systems, Intel, Hewlett Packard Enterprise, and Cohesity are just a few other large companies that integrate AI and edging on another level. 

Conclusion

Edge computing is revolutionizing data processing by bringing computation and storage closer to where they’re needed. It helps reduce latency and enhance reliability across critical applications like healthcare and autonomous vehicles. Its integration with AI facilitates real-time decision-making and deeper insights, driving innovation in industries from healthcare to manufacturing and beyond. As the market grows and technologies evolve, edge computing, along with AI, continues to demonstrate its pivotal role in shaping the future of digital infrastructure.