Posts Tagged llm

Kubernetes has transformed cloud infrastructure by enabling scalable, containerized applications. While it initially gained traction for managing web applications and microservices, its capabilities now extend to AI/ML workloads, making it the go-to platform for data scientists and machine learning engineers.

Running AI/ML workloads on Kubernetes presents unique challenges, including:

• Specialized hardware requirements (e.g., GPUs, TPUs)
• Scalability for model training and inference
• Complex data pipelines that integrate various cloud services
• Infrastructure automation for seamless deployment

Google Cloud Kubernetes (GKE) provides a robust foundation for AI/ML workloads, but managing infrastructure manually can be cumbersome. This is where Pulumi comes in—enabling Infrastructure as Code (IaC) to automate and simplify AI/ML infrastructure on Kubernetes.

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We built Pulumi Copilot to automate a broad spectrum of cloud management activities using the power of LLMs. Since its initial release earlier this year, hundreds of customers have used Pulumi Copilot to understand and manage cloud infrastructure more effectively and securely, and it is only getting better by the day.

Today, we’re excited to announce the availability of the Pulumi Copilot REST API. This new API exposes the full power of Pulumi Copilot, enabling you to integrate infrastructure AI into your own tools, applications, and platforms. While currently in preview, we are eager to get your feedback to ensure it works for anything you can dream up.

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AI is impacting almost every industry today, and for good reason - we are seeing fundamentally new experiences being made possible across a wide variety of products, and a set of new AI capabilities that promise even more incredible change in the near future.

Software development is among the earliest and most prominent fields to realize the benefits of AI, evidenced by the rapid adoption of tools like Github Copilot which is now one of the most heavily adopted developer tools of all time. Developers are benefiting from an incredible increase in their productivity with better scale and faster time to market.

We’re seeing the impacts of AI in the cloud Infrastructure development space in two impactful and complimentary directions:

• 🤖➜☁️: AI is transforming how we author, build and manage cloud infrastructure
• ☁️➜🤖: Cloud infrastructure tooling is changing how we build and deliver AI-based applications

At Pulumi, we’ve already seen profound impacts from AI in both of these directions.

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Earlier this year we launched Pulumi AI, a purpose-built AI assistant that can create Infrastructure as Code (IaC) from natural language prompts using Pulumi. Since launch, we’ve seen incredible adoption of Pulumi AI, with over 200,000 questions asked so far and growing fast. Pulumi AI is popular with users new to Pulumi and/or new to the Cloud, but also heavily used by many of the most advanced IaC users and organizations to quickly discover solutions to new problems they need to solve. Over the last few months, we’ve driven major improvements to Pulumi AI through the recently launched Pulumi AI Answers pages with thousands of AI generated answers to common questions, improvements to code generation correctness and performance, and expansion of the languages supported by Pulumi AI.

Today, we are taking the next big step, introducing support for deploying cloud infrastructure directly from Pulumi AI. Going from idea to running cloud infrastructure is just a natural language prompt away!

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Running models from Hugging Face on Amazon SageMaker is a popular deployment option for AI/ML services. While the SageMaker console allows for provisioning these cloud resources, this deployment pattern is labor intensive to document and vulnerable to human errors when reproducing as a regular operations practice. Infrastructure as Code (IaC) offers a reliable and easy to duplicate deployment practice. By developing this IaC with Pulumi, practitioners can choose to write their infrastructure code in Python and seamlessly develop both AI application code and IaC code in the same language.

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The AI industry is stealing the show as tech’s goldrush of the ’20s. Just looking at ChatGPT’s record setting user growth, and rapid 3rd party integration by top brands, it is not surprising the hype suggests this is the beginning of a major digital transformation.

However, using AI/ML in your own products has some major challenges and obstacles. Below is a diagram of the end to end workflow of building and using an AI model: preparing the data, training a model, fine-tuning a model, hosting and running a model, building a backend service to serve the model, and building the user interface that interacts with the model. Most AI engineers are only involved in a few steps of the process. However, there is one challenge that is common across the entire workflow: creating and managing the cloud infrastructure is hard.

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We recently released Pulumi AI, a purpose-built AI Assistant that can create cloud infrastructure using Pulumi. It builds on the power of Large Language Models (LLMs) and GPT to dramatically reduce the time it takes to discover, learn and use new cloud infrastructure APIs.

We’ve seen amazing engagement and stories from Pulumi users about the impact this tool has had for them over the past few weeks. In this post, we’ll dive deeper into this new technology, and share why we and so many other Pulumi users are so excited about Pulumi AI.

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We’ve seen incredible acceleration of cloud adoption over the past 5 years. Pulumi’s flagship open source IaC solution gives engineers great tools to scale up their cloud infrastructure using the same programming languages and tools they already know and love. As a result, thousands of companies of every size and scale have adopted Pulumi as a lynchpin of their cloud infrastructure strategy.

Today we’re excited to announce Pulumi Insights, the next major productivity enhancement for infrastructure as code. Pulumi Insights provides intelligence, search, and analytics over any infrastructure, in any cloud across your organization, leveraging the latest advances in generative AI and Large Language Models (LLMs). Whether you have an AWS VPC, a Kubernetes CRD, or a DataDog alarm definition, Pulumi Insights enables you to intelligently find and interact with all of your resources from within the Pulumi Cloud.

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