We can all be AI engineers – and we can do it with open source models

The article discusses the increasing accessibility of AI engineering, stating that the barriers to entry are rapidly diminishing. The author, who has experience in DevOps, MLOps, and GenAI, notes that the tools have improved to the point where anyone who can handle an Integrated Development Environment (IDE) and push YAML to git is qualified. He outlines six building blocks for creating an AI application: Models, Prompts, Knowledge, Integrations, Tests, and Deployment.

The author also introduces "AISpec", a YAML file that integrates all these elements in a way that is familiar to those who have worked with modern infrastructure tools. He emphasizes that using open source models allows for data privacy and compliance with regulations like GDPR. The author concludes by stating that anyone with knowledge of version control and basic deployment workflows can build production-ready AI applications, and encourages readers to explore the standard format proposed at aispec.org.

Key takeaways:

The barriers to AI engineering are falling rapidly due to the simplification of complex tools and standardization of workflows.
Building an AI application involves six building blocks: Models, Prompts, Knowledge, Integrations, Tests, and Deployment.
Open source models allow for data privacy, ensuring that your data doesn't end up training someone else's model, which is crucial for companies concerned about regulations like GDPR.
With knowledge of version control and basic deployment workflows, anyone can build production-ready AI applications without needing a PhD or any special skills.

We can all be AI engineers – and we can do it with open source models

Key takeaways:

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