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The AI Infrastructure Guide for Software Development

  • AI Infrastructure
  • Artificial Intelligence (AI)
  • Machine Learning
  • Code Generation
  • Coding Assistants
  • Enterprise Software
To run enterprise-grade AI/ML programs, you need more than just a model--you need a functional data pipeline for your dataset(s), compute resources, and a clear business plan. This collection of resources covers what you need to ensure your projects start on the right foot.
The AI Infrastructure Guide for Software Development

What's In This Guide

Insights & Challenges

  • Article

AI's Data Pipeline Future

While enterprises have a well-documented challenge with costly compute resources, the most immediate hurdle ahead could be creating, building, and operating pipelines for data.

The Data Pipeline is the New Secret Sauce

AI inference infrastructure is fragmented due to cost, compute shortages, performance tradeoffs, and data challenges. Here’s how we find a clearer path forward.

  • Article

The Adoption Problem

What blocks enterprises from rapidly adoption AI tools--and what's stopping ambitious AI startups from closing more enterprise customers? This in-depth interview goes over challenges around economics, compliance, and affordances.

Enterprise AI Infrastructure: Compliance, Risks, Adoption

Successfully deploying AI infrastructure means resolving the tension between risk, innovation, and incrementality. Infrastructure veteran Jos Boumans explains.

  • Article

Understanding Maturity in AI Programs

This comprehensive interview covers the way enterprises mature as they proceed along the path to implementing successful AI programs, considering resourcing, performance, and advanced configurations.

Enterprise AI Infrastructure: Privacy, Maturity, Resources

Planning enterprise infrastructure successfully means considering privacy, resourcing, and what’s to come. BentoML founder Chaoyu Yang explains.

AI in Dev Processes

  • Article

AI Tooling in Development

What does AI tooling mean for the future of writing code? Experts from AI software startups discuss how coding assistants and other AI tools are changing coding, hiring, and the software development profession.

The Future of Coding in the Age of GenAI

What does AI-assisted coding mean for the future of software development? Experts in software and AI tooling weigh in.

  • Article

The Hidden Opportunities of GenAI

This interview provides perspective on how dev teams can skip the hype cycle and unearth new opportunities and use cases in GenAI.

AI’s Hidden Opportunities: Shawn "swyx" Wang on New Use Cases and Careers

Shawn “swyx” Wang discusses the hidden opportunities in AI, including new use cases and new opportunities for aspiring AI engineers.

  • Article

Using Guardrails for LLM Development

Developing AI applications on top of LLMs means safety risks in terms of security, privacy, bias, and of course, hallucinations. This guide covers best practices for using guardrails when building in LLM apps.

How LLM Guardrails Reduce AI Risk in Software Development

LLM guardrails are a powerful tool for reducing the risks of AI in software development. Here's how they can help.

AI Infrastructure References

  • Article

How AI Inference Works

This engineer's guide to AI inference covers the nuts and bolts of the inference process, resourcing requirements, and how inferencing fundamentally differs from training and fine-tuning.

AI Inference: A Guide for Founders and Developers

AI inference isn’t the same as training/fine-tuning machine learning AI models. Here’s what it means for hardware/GPUs, and for developing LLM applications.

  • Article

The Life Cycle of a ML Project

This guide breaks down the steps in a typical machine learning project, including scoping a valuable use case, preparing data, model engineering, deployment, and maintenance.

Machine Learning Lifecycle: Take Projects from Idea to Launch

Discover the ML lifecycle and learn how to take an ML project from idea to launch.

  • Article

The ML Monitoring Process

This reference covers model monitoring for ML projects from top to bottom, including process steps and a full list of metrics.

Machine Learning Model Monitoring: What to Do In Production

Learn how to monitor ML models once they’re in production with these best practices, metrics, and challenges to avoid.

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