Source URL: https://cloud.google.com/blog/products/application-modernization/building-a-modern-ai-ready-developer-platform/
Source: Cloud Blog
Title: Three steps in mapping out your modern platform strategy
Feedly Summary: As AI adoption speeds up, one thing is becoming clear: the developer platforms that got you this far won’t get you to the next stage. While yesterday’s platforms were awesome, let’s face it, they weren’t built for today’s AI-infused application development and deployment. And organizations are quickly realizing they need to update their platform strategies to ensure that developers — and the wider set of folks using AI — have what they need for the years ahead.
In fact, as I explore in a new paper, nine out of ten decision makers are prioritizing the task of optimizing workloads for AI over the next 12 months. Problem is, given the pace of change lately, many don’t know where to start or what they need when it comes to modernizing their developer platforms.
What follows is a quick look at the key steps involved in planning your platform strategy. For all the details, download my full guide, Three pillars of a modern, AI-ready platform.
Step 1. Define your platform’s purpose
Whether you’re building your first platform or your fiftieth, you need to start by asking, “Why?” After all, a new platform is another asset to maintain and operate —you need to make sure it exists for the right reasons.
To build your case, ask yourself three questions:
Who is the platform for? Your platform’s customers, or users, can include developers, architects, product teams, SREs and Ops personnel, data scientists, security teams, and platform owners. Each has different needs, and your platform will need to be tailored accordingly.
What are its goals? Work out what problems you’re trying to solve. For example, are you optimizing for AI? Striving to speed up software delivery? Increasing developer productivity? Improving scale or security? Again, different goals will lead you down different paths for your platform — so map them out right from the start.
How will you measure success? To prove the worth of your platform, and to convince stakeholders to invest in its ongoing maintenance, establish metrics from the outset, and keep on measuring them! These could range from improved customer satisfaction to faster time-to-resolution for support issues.
Step 2. Assemble the pieces of your platform
Now that you’re clear on the customers, goals, and performance metrics of the platform you need, it’s time to actually build the thing. Here’s a glance at the key components of a modern, AI-ready platform — complete with the capabilities developers need to hit the ground running when developing AI-powered solutions.
For a detailed breakdown of what to consider in each area of your platform, including a list of technology options for each category, head over to the full paper.
Step 3. Establish a process for improving your platform
The journey doesn’t end once your platform’s built. In fact, it’s just beginning. A platform is never “done;” it’s just released. As such, you need to adopt a continuous improvement mindset and assign a core platform team the task of finding new ways to introduce value to stakeholders.
At this stage, my top tip is to treat your platform like a product, applying platform engineering principles to keep making it faster, cheaper, and easier to deliver software. Oh, and to leverage the latest in AI-driven optimization tools to monitor and maintain your platform over time!
Ready to start your platform journey?
Organizations embark on platform overhauls for a whole bunch of reasons. Some do it to better cope with forecasted growth. Others have AI adoption in their sights. Then there are those driven by cost, performance, or the user experience. Whatever your reason for getting started, I encourage you to read the full paper on building a modern AI-ready platform — your developers (and the business) will thank you.
AI Summary and Description: Yes
Summary: The provided text emphasizes the urgent need for organizations to modernize their developer platforms in response to the rapid adoption of AI technologies. It outlines a strategic approach to redefining platform purposes, assembling necessary components, and fostering continuous improvement, presenting actionable insights for professionals in AI and cloud computing.
Detailed Description:
The passage addresses the transformation required in developer platform strategies to effectively leverage AI technologies in application development and deployment. Here are the significant points highlighted:
– **Need for Modernization**: Organizations recognize that legacy platforms are inadequate for meeting contemporary AI demands. This acknowledgment is prompting decision-makers to prioritize the optimization of workloads for AI within the next year.
– **Steps for Platform Strategy**:
1. **Define Platform Purpose**:
– Identify the target audience for the platform (e.g., developers, security teams, data scientists).
– Establish clear goals for the platform, such as optimizing for AI, enhancing security, or improving software delivery timelines.
– Define success metrics from the outset to justify ongoing investment and maintenance.
2. **Assemble Platform Components**:
– Build an AI-ready platform by incorporating essential components tailored to the needs of users. A comprehensive approach is needed to ensure that developers have the necessary capabilities to create AI-powered solutions.
3. **Establish Continuous Improvement**:
– Acknowledge that platform development is an ongoing process. Continuous improvement should be prioritized through a dedicated team focusing on enhancing platform performance and user value.
– Treat the platform as a product, applying platform engineering principles and utilizing AI-driven tools for sustained optimization.
– **Diverse Motivations for Modernization**:
– Organizations undertake platform renovations for various reasons, including anticipated growth, AI integration, cost-efficiency, performance enhancement, and improved user experiences.
Overall, the text serves as a guide for best practices in creating frameworks that support AI technologies, emphasizing strategic planning, user-centric design, and iterative development. Security and compliance professionals in particular should consider these guidelines to ensure that security protocols are effectively integrated into modernized platforms, thereby addressing potential vulnerabilities as AI capabilities expand.