Machine learning pipelines are vulnerable to data and infrastructure errors that can disrupt production. By implementing smoke tests with both random and controlled synthetic data, teams can validate pipeline functionality and schema adherence before running full-scale jobs. This practice supports continuous integration and delivery, leading to fewer outages and more reliable deployments.

For better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation will discuss ways that enterprise scale organizations are addressing these challenges to safely and securely build these development environments.

Building better agents fast: real stories, lean workflows, and practical tips for building trustworthy, human-friendly agents in accounting and beyond.