Part 3: Production-ready Machine Learning with LightGBM

In Part 3, we will delve into the practical applications of ML solutions in production environments. We will uncover the intricacies of machine learning pipelines, ensuring systematic data processing and model building for consistent results. MLOps, a confluence of DevOps and ML, takes center stage, highlighting the importance of deploying and maintaining robust ML systems in real-world scenarios. Through hands-on examples, we will explore the deployment of ML pipelines on platforms (like Google Cloud, Amazon SageMaker, and the innovative PostgresML) emphasizing the unique advantages each offers. Lastly, distributed computing and GPU-based training will be explored, showcasing methods to expedite training processes and manage larger datasets efficiently. This concluding part will emphasize the seamless integration of ML into practical, production-ready solutions, equipping readers with the knowledge to bring their models to life...