AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

I share a novel framework called MM Life Circles, which allow teams to visualize and manage the entire lifecycle of generative AI models, from research to deployment including best practices for handover between each phase.

So essentially, I start with choosing a generative AI model as an example, then cover the data side of it followed by training frameworks.

• Comprises of research platforms such as Python, R studio and associated libraries
• Covers model building and tuning frameworks such as Pytorch Lightning, Weights & Biases, mlflow etc.
• Benchmarking & Optimization Tools such as onnx, tensorflow, tensorRT optimization