In this episode of the "MLOps Weekly" podcast, host Simba Khadder talks with Paul Iusztin, a Senior ML and MLOps Engineer at Decoding ML, about his journey from software engineering to MLOps. They discuss the integration of software engineering principles in ML, the challenges of writing tests for ML applications, and the key differences between software and ML engineering. Paul shares insights on building scalable and reproducible MLOps platforms, emphasizing the importance of decoupling feature, training, and inference pipelines. They also explore the convergence of MLOps and LLMOps, highlighting the unique aspects of prompt engineering. The conversation underscores the importance of robust engineering practices and continuous adaptation in the rapidly evolving AI landscape.