Practical Deep Learning at Scale with MLflow

Train test run track store tune deploy and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey FeaturesFocus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train run tune and deploy deep learning pipelines with explainability and reproducibilityBook DescriptionThe 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.From 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. Youll 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 youll 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.By the end of this book youll 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.What you will learnUnderstand MLOps and deep learning life cycle developmentTrack deep learning models code data parameters and metricsBuild deploy and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is forThis 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.