August 8, 2024
August 9, 2024
August 8, 2024
August 9, 2024
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In the rapidly evolving landscape of cloud computing and software development, a revolutionary approach is emerging that promises to redefine how we manage and optimize our digital infrastructure: Autonomous Platform Engineering. This cutting-edge paradigm leverages artificial intelligence (AI) and machine learning (ML) to create self-managing, self-optimizing systems that can handle the complexities of modern cloud environments with unprecedented efficiency and effectiveness.
To appreciate the significance of Autonomous Platform Engineering, it's essential to understand its evolution:
This progression represents a continuous shift towards greater efficiency, scalability, and automation in managing digital infrastructure. We have seen Gartner recognize the role of Ai in platform engineering with the inclusion of "AI Augmented Software Engineering" in the first ever Platform Engineering Hype Cycle (shown below, originally shared on Linkedin by Manju Bhat here).
Autonomous Platform Engineering comprises several crucial elements that work in concert to create a self-managing cloud ecosystem:
One of the most significant advantages of Autonomous Platform Engineering is its ability to optimize cloud resources continuously. This is particularly crucial as organizations grapple with the challenge of managing costs while maintaining high performance in increasingly complex cloud environments.
At the forefront of the Autonomous Platform Engineering revolution is Sedai, a company that has developed a cutting-edge autonomous cloud management platform. Sedai's approach exemplifies the potential of this new paradigm, offering unique insights into how these systems can be implemented effectively.
The adoption of Autonomous Platform Engineering, as exemplified by solutions like Sedai, is having a profound impact on organizations and the role of platform engineers:
For platform engineers specifically:
While platform engineers are at the forefront of implementing autonomous systems, the benefits extend across multiple teams within an organization. Here's how different roles can leverage and benefit from autonomous platform engineering:
By implementing autonomous platform engineering solutions like Sedai, organizations can create a more efficient, collaborative, and innovative environment where each team can focus on their core competencies and strategic objectives.
While the benefits of Autonomous Platform Engineering are significant, its adoption comes with challenges that organizations need to address:
To successfully adopt Autonomous Platform Engineering, consider the following best practices:
To illustrate the transformative power of Autonomous Platform Engineering, let's look at how Palo Alto Networks, a global cybersecurity leader, has implemented many of these principles in their cloud infrastructure management.
Palo Alto Networks has embraced autonomous platform engineering to manage its vast and complex cloud infrastructure, which includes over 50,000 microservices across multiple cloud providers. Faced with rapid growth, increasing cloud spend, and the risk of team burnout, they developed an autonomous platform with a clear vision: to leverage production data fully and autonomously, providing best-in-class SRE support while achieving sub-linear growth in resources. In the chart below, multiple platform capabilities were identified by Palo Alto Networks are being able to be operated autonomously.
Their approach focused on four key operational excellence goals: reducing mean time to detect and resolve issues, improving performance, and managing costs. To achieve this, Palo Alto Networks implemented autonomous optimization capabilities for both serverless and Kubernetes environments. For serverless functions, they deployed AI-driven systems that continuously optimize memory and CPU settings, manage concurrency, and adapt to new releases. In their Kubernetes environment, they implemented intelligent scaling, infrastructure rightsizing, and cost-optimized purchasing strategies.
The results of this autonomous approach have been significant. In their serverless environment, Palo Alto Networks achieved a 22% latency improvement and an 11% cost reduction. For Kubernetes, they've already realized 2% cost savings with a potential for 61% further savings identified. Beyond these quantitative improvements, the autonomous platform has reduced the operational burden on their SRE team, allowing them to focus on more strategic initiatives. This real-world implementation demonstrates the transformative potential of autonomous platform engineering in managing complex, large-scale cloud infrastructures.
You can watch more about Palo Alto Network's experience here.
As we look ahead, several trends are likely to shape the evolution of Autonomous Platform Engineering:
Autonomous Platform Engineering represents a paradigm shift in how we approach cloud infrastructure management. By leveraging AI and ML, organizations can achieve unprecedented levels of efficiency, performance, and cost-effectiveness in their cloud operations.
As pioneers in this field, companies like Sedai are paving the way for a future where cloud infrastructures are not just automated, but truly autonomous – continuously learning, adapting, and optimizing without constant human intervention. The real-world success of Palo Alto Networks demonstrates that this future is not just theoretical but achievable and highly beneficial.
For platform engineers and organizations alike, embracing Autonomous Platform Engineering means not just adapting to new technologies, but reimagining the very nature of cloud management. It's an opportunity to shift focus from routine operations to strategic innovation, driving business value and staying ahead in an increasingly competitive digital landscape.
The journey towards fully autonomous cloud environments is just beginning, and the possibilities are boundless. As we continue to push the boundaries of what's possible with AI and ML in cloud management, one thing is clear: Autonomous Platform Engineering is not just a trend – it's the future of cloud infrastructure management, and it's here to stay.
Are you ready to embrace the autonomous future of platform engineering? Take the next step in revolutionizing your cloud infrastructure management by experiencing Sedai's autonomous capabilities firsthand. Sign up for a personalized demo today, and discover how Sedai can accelerate your platform engineering efforts, optimize your cloud resources, and free your team and the teams you support to focus on strategic innovation.
August 9, 2024
August 8, 2024
In the rapidly evolving landscape of cloud computing and software development, a revolutionary approach is emerging that promises to redefine how we manage and optimize our digital infrastructure: Autonomous Platform Engineering. This cutting-edge paradigm leverages artificial intelligence (AI) and machine learning (ML) to create self-managing, self-optimizing systems that can handle the complexities of modern cloud environments with unprecedented efficiency and effectiveness.
To appreciate the significance of Autonomous Platform Engineering, it's essential to understand its evolution:
This progression represents a continuous shift towards greater efficiency, scalability, and automation in managing digital infrastructure. We have seen Gartner recognize the role of Ai in platform engineering with the inclusion of "AI Augmented Software Engineering" in the first ever Platform Engineering Hype Cycle (shown below, originally shared on Linkedin by Manju Bhat here).
Autonomous Platform Engineering comprises several crucial elements that work in concert to create a self-managing cloud ecosystem:
One of the most significant advantages of Autonomous Platform Engineering is its ability to optimize cloud resources continuously. This is particularly crucial as organizations grapple with the challenge of managing costs while maintaining high performance in increasingly complex cloud environments.
At the forefront of the Autonomous Platform Engineering revolution is Sedai, a company that has developed a cutting-edge autonomous cloud management platform. Sedai's approach exemplifies the potential of this new paradigm, offering unique insights into how these systems can be implemented effectively.
The adoption of Autonomous Platform Engineering, as exemplified by solutions like Sedai, is having a profound impact on organizations and the role of platform engineers:
For platform engineers specifically:
While platform engineers are at the forefront of implementing autonomous systems, the benefits extend across multiple teams within an organization. Here's how different roles can leverage and benefit from autonomous platform engineering:
By implementing autonomous platform engineering solutions like Sedai, organizations can create a more efficient, collaborative, and innovative environment where each team can focus on their core competencies and strategic objectives.
While the benefits of Autonomous Platform Engineering are significant, its adoption comes with challenges that organizations need to address:
To successfully adopt Autonomous Platform Engineering, consider the following best practices:
To illustrate the transformative power of Autonomous Platform Engineering, let's look at how Palo Alto Networks, a global cybersecurity leader, has implemented many of these principles in their cloud infrastructure management.
Palo Alto Networks has embraced autonomous platform engineering to manage its vast and complex cloud infrastructure, which includes over 50,000 microservices across multiple cloud providers. Faced with rapid growth, increasing cloud spend, and the risk of team burnout, they developed an autonomous platform with a clear vision: to leverage production data fully and autonomously, providing best-in-class SRE support while achieving sub-linear growth in resources. In the chart below, multiple platform capabilities were identified by Palo Alto Networks are being able to be operated autonomously.
Their approach focused on four key operational excellence goals: reducing mean time to detect and resolve issues, improving performance, and managing costs. To achieve this, Palo Alto Networks implemented autonomous optimization capabilities for both serverless and Kubernetes environments. For serverless functions, they deployed AI-driven systems that continuously optimize memory and CPU settings, manage concurrency, and adapt to new releases. In their Kubernetes environment, they implemented intelligent scaling, infrastructure rightsizing, and cost-optimized purchasing strategies.
The results of this autonomous approach have been significant. In their serverless environment, Palo Alto Networks achieved a 22% latency improvement and an 11% cost reduction. For Kubernetes, they've already realized 2% cost savings with a potential for 61% further savings identified. Beyond these quantitative improvements, the autonomous platform has reduced the operational burden on their SRE team, allowing them to focus on more strategic initiatives. This real-world implementation demonstrates the transformative potential of autonomous platform engineering in managing complex, large-scale cloud infrastructures.
You can watch more about Palo Alto Network's experience here.
As we look ahead, several trends are likely to shape the evolution of Autonomous Platform Engineering:
Autonomous Platform Engineering represents a paradigm shift in how we approach cloud infrastructure management. By leveraging AI and ML, organizations can achieve unprecedented levels of efficiency, performance, and cost-effectiveness in their cloud operations.
As pioneers in this field, companies like Sedai are paving the way for a future where cloud infrastructures are not just automated, but truly autonomous – continuously learning, adapting, and optimizing without constant human intervention. The real-world success of Palo Alto Networks demonstrates that this future is not just theoretical but achievable and highly beneficial.
For platform engineers and organizations alike, embracing Autonomous Platform Engineering means not just adapting to new technologies, but reimagining the very nature of cloud management. It's an opportunity to shift focus from routine operations to strategic innovation, driving business value and staying ahead in an increasingly competitive digital landscape.
The journey towards fully autonomous cloud environments is just beginning, and the possibilities are boundless. As we continue to push the boundaries of what's possible with AI and ML in cloud management, one thing is clear: Autonomous Platform Engineering is not just a trend – it's the future of cloud infrastructure management, and it's here to stay.
Are you ready to embrace the autonomous future of platform engineering? Take the next step in revolutionizing your cloud infrastructure management by experiencing Sedai's autonomous capabilities firsthand. Sign up for a personalized demo today, and discover how Sedai can accelerate your platform engineering efforts, optimize your cloud resources, and free your team and the teams you support to focus on strategic innovation.