Posts Tagged iac

As a skeptic of “quick starts” myself, I approach most marketing promises with a measure of cautious excitement. If the great and powerful algorithm, friends, or a peer brought your attention here, then I invite you to take this one seriously.

Pulumi, with its full support of many general-purpose programming languages, can appear like a chore to get started with. The feeling can haunt seasoned developers as much as practitioners new to infrastructure code.

However, I’ll show you that finding the proverbial easy street is easier than you might believe. The pulumi new developer story just gets sweeter when combined with a few other nice-to-have conveniences.

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In 2024, several trends will dominate cloud computing, driving innovation, efficiency, and scalability. From Infrastructure as Code (IaC) to AI/ML, platform engineering to multi-cloud and hybrid strategies, and security practices, let’s explore the 10 biggest emerging trends.

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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.

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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.

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