{"product_id":"practical-deep-learning-at-scale-with-mlflow-bridge-the-gap-between-offline-experimentation-and-online-production-paperback","title":"Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production - Paperback","description":"\u003cp\u003eby \u003cb\u003eYong Liu\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTrain, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFocus on deep learning models and MLflow to develop practical business AI solutions at scale\u003c\/li\u003e\n\u003cli\u003eShip deep learning pipelines from experimentation to production with provenance tracking\u003c\/li\u003e\n\u003cli\u003eLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.\u003c\/p\u003e\u003cp\u003eFrom there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand MLOps and deep learning life cycle development\u003c\/li\u003e\n\u003cli\u003eTrack deep learning models, code, data, parameters, and metrics\u003c\/li\u003e\n\u003cli\u003eBuild, deploy, and run deep learning model pipelines anywhere\u003c\/li\u003e\n\u003cli\u003eRun hyperparameter optimization at scale to tune deep learning models\u003c\/li\u003e\n\u003cli\u003eBuild production-grade multi-step deep learning inference pipelines\u003c\/li\u003e\n\u003cli\u003eImplement scalable deep learning explainability as a service\u003c\/li\u003e\n\u003cli\u003eDeploy deep learning batch and streaming inference services\u003c\/li\u003e\n\u003cli\u003eShip practical NLP solutions from experimentation to production\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 288\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.6 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 08, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42120303640711,"sku":"9781803241333","price":70.54,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/fa34140351241344c78da3a2d2601ddc.webp?v=1732546414","url":"https:\/\/booksby.splitshops.com\/products\/practical-deep-learning-at-scale-with-mlflow-bridge-the-gap-between-offline-experimentation-and-online-production-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}