Practical Deep Learning at Scale with MLflow: Bridge the Gap Between Offline Experimentation and Online Production

Track, store, versionize, and deploy deep learning models and model pipelines for different use casesKey FeaturesFocus on deep learning models and frameworks that solve practical problemsExplore detailed example applications using deep learning models and pipeline managementLearn to train, test, and tune pipelines at scale and implement model explainabilityBook DescriptionDeep learning models used for solving practical problems can be developed, managed, shipped, and refined with their code and data using MLflow.This book teaches you how to build reproducible ML pipelines and run them in a local or cloud environment. With the help of easy-to-use frameworks provided by MLflow, you'll bridge the gap between diverse environments. You'll find out how to switch between different frameworks, track your code, create versions of a pipeline, and model along with its parameters and metrics using MLflow's tracking and registry APIs and Delta Lake. Next, you'll customize the model pipeline to save and load the model to decouple offline model experimentation and production, all while keeping the model's behavior consistent with different data sets managed by Delta Lake. The book also helps you to recognize the patterns of different inference pipelines and shows you how to implement them. Finally, you'll be able to figure out how to choose the right framework for the right model serving scenarios and enjoy the best model serving experience in a scalable and cost-effective way.By the end of this deep learning book, you'll be able to build and deploy your own deep learning applications using MLflow and deep learning model libraries, addressing the key pain points encountered in the deep learning model development life cycle.What you will learnTrack deep learning models in different environments using MLflowBuild, deploy, and run deep learning model pipelinesUnderstand the key challenges in the deep learning model development life cycleManage the data dependencies and data versioning associated with deep learning modelsScale up deep learning model pipeline training, testing, tuning, and deploymentShip practical NLP and image deep learning solutions from experimentation to productionWho This Book Is ForThis book is for machine learning practitioners including data scientists, data engineers, and ML engineers and scientists interested in building scalable deep learning models using MLflow. Basic understanding of data science and machine learning is necessary to understand th