The integration of artificial intelligence (AI) with DevOps signals a new era in software development. DevOps possesses unique characteristics and needs that make it exceptionally compatible with AI augmentation. Given that code fundamentally relies on language, and large language models (LLMs) serve as the core of GPT functionality, these models are particularly well-suited for tasks such as code generation. This article unwraps the topics addressed during our “AI: Friends or Foe | AI Talks for DevOps” event in San Francisco.
The emergence of DevOps revolutionized software development. Now, with AI powered tools like LangChain, these transformations are being accelerated. Unsurprisingly, our distinguished speaker at the launch of Pulumi’s in-person AI Talks, Patrick Debois, who coined the term “DevOps,” has recently tuned into LLM and GenAI Ops using the Langchain framework.
The process of taking an idea and turning it into reality has been nothing short of extraordinary since we started innovating with Artificial Intelligence. With this technology, machines learn about and communicate with people, while also helping us in ways we never could have imagined only a few years ago. If you’ve been following along, you might recall our previous article where we used Python and Pulumi to a chatbot API (named katwalk) to the cloud.
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.
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.