<p>This article was <a href="https://vmblog.com/archive/2024/01/25/upbound-2024-predictions-the-standardization-of-ai-tooling-and-platforms.aspx" target="_blank">previously published on VMblog here</a>.</p><p>With the rise of hot artificial intelligence (AI) tools, every company and person seemed to be experimenting with AI in 2023. There is no going back: AI will continue to play an evolving role.</p><h2>AI and platforms</h2><p>I predict that 2024 will be more about standardization on AI tooling and platforms. It's still the Wild Wild West with AI, but with more maturity will come more standardization and out of the box solutions.&nbsp;</p><p>There will be a great reckoning. We're going to see a lot of the "garbage" that exists today-like low-value UX/UI on top of other platforms (like on top of ChatGPT)-will basically go away. This will happen since it'll get much easier for any platform engineer to run these AI stacks themselves.</p><p>Here's what other members of the Upbound team contemplated regarding the potential impact of AI on businesses this year.</p><h2>AI and candidate-driven job matching</h2><p>Lem Diaz, head of people at Upbound, says:</p><blockquote><p>"I predict that AI will become more prevalent in conversational job matching. Job seekers will begin to see more interactive tools to help them navigate the front end of their job-seeking journeys. This may include things like asking questions about the company's culture, strategy, competitive landscape, tech stack, funding, and other factors and getting a list of matches. These interactive models will enrich a job seeker's understanding of their potential fit and whether or not to pursue a position at a company. This will begin the shift from employer-driven job matching to candidate-driven job matching. It'll still be nascent in 2024, but I believe it will mark the beginning of this shift powered by AI."</p></blockquote><h2>AI makes marketing less tedious</h2><p>Kelsey Havens, head of marketing at Upbound, notes:</p><blockquote><p>"AI will become less of a buzzword in marketing and more of an integrated feature in existing marketing tools and technology. Tedious and repetitive tasks like ad sizing, sequence writing, tagging and task management will be offloaded to AI, giving marketers more mental space and time to be creative and strategic."</p></blockquote><h2>Cloud-native, multi-cloud infrastructure powers AI and ML</h2><p>Tom Anthony, vice president of global sales and customer success at Upbound, adds:&nbsp;</p><blockquote><p>"In 2024, AI will make its way deeper into enterprises. I see this evolving in two ways. Cloud-native, multi-cloud infrastructure will be the fabric that enterprises standardize on to power their AI/ML initiatives. And, "AI-as-a-Service"- the ability for businesses to access AI tools and capabilities without having to invest in their own AI infrastructure or develop expertise-will become the way to drive faster innovation, productivity and cost savings within the enterprise."</p></blockquote><p>It is clear the teams building the underlying infrastructure powering platforms, including AI and ML, have played an increasingly important role in companies. Here at Upbound, we're about making the lives of platform teams easier while helping them meet security, compliance, and policy standards. I'm convinced that creating platforms powered by control planes is a crucial step towards enhanced efficiency-a need that becomes more pressing as AI permeates all facets of the business. I talk more about this in a video on how to <a href="https://resources.upbound.io/community/unleash-the-potential-of-your-aiml-platform-with-a-crossplane-control-plane" target="_blank">unleash the potential of your AI/ML platform with a Crossplane control plane</a>.</p><p>To learn more about how Upbound can enable you to standardize AI/ML infrastructure with reference architectures that are easily deployed and meet security, compliance, and policy requirements, <a href="https://blog.upbound.io/ai-ml/">view this blog here</a>.</p>

The Standardization of AI Tooling and Platforms

<p>Companies are investing heavily in Artificial Intelligence and Machine Learning (AI/ML) to improve their products, accelerate their processes, and get an edge on their competitors. However, the AI landscape of software, tools, and techniques is growing larger by the hour. Fast and safe provisioning of these environments throughout their lifecycle could be the key to making it all work. Let’s take a look at how you can build a platform to <a href="https://www.upbound.io/solutions/ai" target="_blank">get ahead in the AI/ML race</a>.</p><h2>The AI Lifecycle</h2><p>The AI landscape is gigantic. The industry has overloaded all of these tools, terms, services, and approaches and they sometimes get mixed together. It can be extremely confusing. So, I should probably start by defining what <strong>the stages of a typical rollout of AI/ML workloads</strong> looks like.</p><p>The process of building AI to support a function in your application or business has three stages.</p><ul><li><strong>The learning stage:</strong> This is where you gather the data that is relevant to the information you want to process.</li><li><strong>The modeling stage:</strong> In this stage, you need to use the data you gathered in order to train a model to be able to predict patterns and answer questions about the data.</li><li><strong>The deployment stage:</strong> Without implementing your model in some way you can’t use the AI to do anything. So, you might deploy your model as a chat bot like chatGPT for example, an image generator, or voice response tool.</li></ul><p>Each of these stages can be further broken down into smaller more specific milestones in the process.</p>

<p>However, you don’t just do this once. You need to continuously improve the quality of the data you are gathering, adjust your model or try new ones, and even perhaps deploy in a different way or in different places depending on how your AI needs to be accessed and used. <strong>This creates a lifecycle</strong> that will go on indefinitely (or until your AI model takes over the world 😀) and it requires managing the infrastructure and services at each stage and iteration for multiple developers and ML engineers.</p><h2>The Challenges With AI/ML Workload Deployments</h2>

<p>This lifecycle and the pace of change in the AI landscape presents multiple challenges when it comes to trying to manage and maintain all the required infrastructure for AI/ML workloads. Here are just a few examples:</p><h3>Maintaining infrastructure consistency</h3><p>AI platforms require deployment across <strong>multiple environments</strong> (development, staging, production) and multiple cloud providers. Ensuring that all these environments are consistent in terms of configuration and infrastructure can be a daunting task.</p><h3>Resource allocation and management</h3><p>AI workloads, especially training deep learning models, require <strong>specific resources</strong> like GPU instances. Efficiently allocating and managing such resources without wastage can be a unique and complex challenge.</p><h3>Complex dependencies and pipelines</h3><p>AI platforms might rely on a mesh of services, databases, and other infrastructure components. Managing dependencies, connectivity, and ensuring that services are set up in the correct order are <strong>critical for reliability</strong>.</p><h3>Security and compliance</h3><p>Ensuring that all infrastructure components of an AI platform adhere to <strong>security best practices and compliance</strong> standards can be a significant concern. This is especially true when teams go rogue and try to do things themselves.</p><h3>Portability across cloud providers</h3><p><strong>Vendor lock-in</strong> is a concern for many organizations. Building an AI platform on one cloud provider and then moving it to another can be a massive undertaking.</p><h3>Scaling infrastructure</h3><p>AI platforms often face variable workloads with real-time processing and large datasets. <strong>Infrastructure needs can spike</strong> during intensive tasks, like model training, and reduce during idle times. Manually provisioning resources doesn’t scale and relying on scripts can lead to configuration drift.</p><h2>Building an AI/ML platform</h2><p>There is no one-size-fits-all solution for AI/ML infrastructure. Every business will need a unique AI/ML infrastructure stack that fits its needs. So, <strong>what’s needed here is a platform</strong> that can help with all of these concerns and make it significantly easier for a platform team to keep up with the relentless demand of requests for AI infrastructure.</p><p>The ideal AI/ML platform should provide:</p><ul><li>Custom APIs that developers can use to deploy resources</li><li>Declarative configurations that can enforce consistency</li><li>A provider agnostic approach to ensure portability</li><li>An ecosystem of integrations for the most common services</li><li>An easy way to integrate new and custom services</li><li>Automatic reconciliation of changes to keep everything up to date</li></ul>

<p>Upbound uses managed control planes to provide a platform that offers easy, consistent deployment of AI/ML infrastructure and services for developers and ML engineers. Upbound addresses each requirement listed above so you don’t have to worry about failed deployments, compliance, or security concerns. By standardizing on a flexible platform with API abstractions there is no need for shadow IT to creep into your environment.</p><p>From data collection and preparation to model deployment and retraining, <strong>Upbound can help at all stages of your AI/ML journey.</strong> With Upbound, you can not only deploy representative and reproducible infrastructure at the beginning of your project but also new services and applications can be added as needed. All this while enabling organizations to balance performance, cost, and developer experience levels when constructing the right infrastructure stack.</p>

<p>To illustrate how it all works we have created an <a href="https://marketplace.upbound.io/configurations/upbound/configuration-caas-ml/latest" target="_blank">example configuration</a> in the Upbound marketplace that you can use with Upbound or Crossplane to <strong>deploy your own AI environment</strong> using Stable Diffusion. This AI application will learn about images of dogs and will be able to produce a reasonable dog portrait on demand. By following the guide in the <a href="https://github.com/upbound/configuration-caas-ml" target="_blank">configuration readme</a> you will learn how to use a control plane to automatically:</p><ul><li>Configure a VPC with subnets and gateways</li><li>Deploy a Kubernetes cluster using AWS EKS</li><li>Configure a load balancer</li><li>Configure the node groups for core components and GPU processing</li><li>Install the NVIDIA Device Plugin</li><li>Configure JupyterHub to facilitate machine learning</li><li>Setup Ingress Nginx</li><li>Install and configure Flux to automate the deployment and continuous synchronization</li><li>Deploy the Kuberay-Operator for scaling</li></ul>

<p>Phew! That is a lot! It is also exactly the reason we need a cloud-native platform like Upbound to help out. It would be difficult enough to do these tasks by yourself one time. Imagine trying to do all this hundreds of times a week!</p><p>To prove that this all works as advertised, here is one of the pictures that was produced by using this configuration to <strong>drastically simplify the process of setting up AI/ML infrastructure</strong> on demand. Try it for yourself and share your favorite AI doggie with the world.</p>

<h2>What's Next?</h2><p>To get started using Ubound as your cloud-native platform for deploying and managing AI/ML workloads, <a href="https://www.upbound.io/register/a" target="_blank">sign up for a free 30-day trial</a> and try out the AI/ML configuration (<a href="https://marketplace.upbound.io/configurations/upbound/configuration-caas-ml/latest" target="_blank">also in the Upbound Marketplace</a>). Be sure to let us know if you have any questions and enjoy all the free doggy portraits!</p>

AI Made Easy: Your Guide to Fast and Reliable AI/ML Workload Deployment

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<p>In today's rapid-paced business ecosystem, AI/ML isn't just an edge—it's essential. Yet, many organizations grapple with the intricacies of setting up, deploying, and operating robust AI/ML infrastructure. Crossplane, a pioneering control plane technology, is the antidote to these challenges. By leveraging a Crossplane Control Plane, you can effortlessly compose and manage your infrastructure in a unified, declarative manner. This not only accelerates the AI/ML development lifecycle but also ensures optimal resource allocation, streamlined workflows, and consistent deployments.</p><p>As you eye the AI frontier, integrating a Crossplane Control Plane isn't just about staying ahead—it's about not getting left behind. The future is unfolding at breakneck speed, and a control plane is the catalyst that ensures companies don't just adapt these new technologies but thrive. In this talk we will discuss:</p><ul><li>Understanding Control Planes and why they are quickly becoming the default technology for their crucial role in building a complex platform, including an AI platform</li><li>How Crossplane and its approach to managing Infrastructure as Data enables organizations to build, deploy, and operate any type of public and private cloud resources effortlessly</li><li>Understanding the AI/ML Pipeline and the journey from data collection to model training to deployment to tuning and all the intricate challenges faced when compiling all this infrastructure together seamlessly to form the basis of your AI platform</li><li>Real-world success stories of accelerated time-to-market by deploying an AI/ML Pipeline with a Crossplane Control Plane</li></ul>