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MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases // Amritha Arun Babu & Abhik Choudhury // #221

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Transitioning to Data Science Concepts and Challenges

Exploring the journey of transitioning from app building to data science, this chapter covers understanding terminology, model architectures, real-time versus batch data processing, and the importance of ground truthing data. It highlights the rise of AI and ML in recent years, the distinctions between data and coding pipelines, and the skills needed for AI product development. The chapter delves into challenges like transitioning to Large Language Models, ensuring compliance and scalability, and the importance of legal considerations in MLOps.

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