Practical Deep Learning at Scale with MLflow
This book teaches you how to use MLflow to support deep learning life cycle development with step-by-step instructions. You’ll build NLP solutions from scratch and implement scalable deep learning pipelines from initial offline experimentation to production with coherent provenance tracking for code, data, models, and explainability.
Offered by
Difficulty Level
Intermediate
Completion Time
9h36m
Language
English
About Book
Who Is This Book For?
This 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.
Practical Deep Learning at Scale with MLflow
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h36m total length
Deep Learning Life Cycle and MLOps Challenges
Getting Started with MLflow for Deep Learning
Tracking Models, Parameters, and Metrics
Tracking Code and Data Versioning
Running DL Pipelines in Different Environments
Running Hyperparameter Tuning at Scale
Multi-Step Deep Learning Inference Pipeline
Deploying a DL Inference Pipeline at Scale
Fundamentals of Deep Learning Explainability
Implementing DL Explainability with MLflow
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