"Machine Learning Engineering with Python" is a practical guide for ML engineers and MLOps practitioners who want to build and deploy scalable and robust machine learning solutions. Published by @PacktPublishing and written by Andrew P. McMahon ( @ElectricWeegie ). The book covers the technical concepts, implementation patterns, and development methodologies for the ML development lifecycle, from data collection and preprocessing to model training and evaluation, to deployment and monitoring. The book explores core MLOps practices, such as hyperparameter optimization, model management, drift detection, and performance monitoring, using various tools and frameworks, such as AWS, TensorFlow, PyTorch, MLflow, and Seldon Core. The book also delves into advanced topics, such as deep learning, generative AI, and large language models (LLMs), and shows how to build a pipeline that leverages LLMs using LangChain, a generative AI platform. The book provides end-to-end examples of deployable ML microservices and pipelines for various use cases, such as sentiment analysis, image classification, and text summarization. I really recommend this book! You can check it in the link below👇
@daansan_ml @PacktPublishing Thank you for the review David, very kind!
@daansan_ml @PacktPublishing @ElectricWeegie Good book - can recommend.
@daansan_ml @PacktPublishing @ElectricWeegie Thankyou for sharing this David