Ready for an AI engineering tip that will blow your mind? Here it is…
Leverage containerization to deploy your AI models more efficiently across different environments. Docker is useful for packaging ML models and their dependencies into containers so that you can run them across different environments. And, by using KubeFlow (machine learning toolkit), you can deploy predictive models directly within Kubernetes containers, without incurring the time and expense of bringing in data engineers.
For a more in-depth training on AI engineering, be sure to join tomorrow’s free live training session where you’ll learn how to build a RAG agent!
What is your number #1 question when it comes to building with generative AI? I’d love to share my knowledge with you so ask away!
Lillian
PS. This tip snippet and live training are generously sponsored by SingleStore.