Machine learning engineering with MLflow manage the end-to-end machine learning lifecycle with MLflow /

Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow Use MLflow to iteratively develop a ML model and manage it Disco...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lauchande, Natu (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2021.
Temas:
Machine learning.
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title
  • Copyright and Credits
  • Table of Contents
  • Section 1: Problem Framing and Introductions
  • Chapter 1: Introducing MLflow
  • Technical requirements
  • What is MLflow?
  • Getting started with MLflow
  • Developing your first model with MLflow
  • Exploring MLflow modules
  • Exploring MLflow projects
  • Exploring MLflow tracking
  • Exploring MLflow Models
  • Exploring MLflow Model Registry
  • Summary
  • Further reading
  • Chapter 2: Your Machine Learning Project
  • Technical requirements
  • Exploring the machine learning process
  • Framing the machine learning problem
  • Problem statement
  • Success and failure definition
  • Model output
  • Output usage
  • Heuristics
  • Data layer definition
  • Introducing the stock market prediction problem
  • Stock movement predictor
  • Problem statement
  • Success and failure definition
  • Model output
  • Output usage
  • Heuristics
  • Data layer definition
  • Sentiment analysis of market influencers
  • Problem statement
  • Success and failure definition
  • Model output
  • Output usage
  • Heuristics
  • Data layer definition
  • Developing your machine learning baseline pipeline
  • Summary
  • Further reading
  • Section 2: Model Development and Experimentation
  • Chapter 3: Your Data Science Workbench
  • Technical requirements
  • Understanding the value of a data science workbench
  • Creating your own data science workbench
  • Building our workbench
  • Using the workbench for stock prediction
  • Starting up your environment
  • Updating with your own algorithms
  • Summary
  • Further reading
  • Chapter 4: Experiment Management in MLflow
  • Technical requirements
  • Getting started with the experiments module
  • Defining the experiment
  • Exploring the dataset
  • Adding experiments
  • Steps for setting up a logistic-based classifier
  • Comparing different models
  • Tuning your model with hyperparameter optimization
  • Summary
  • Further reading
  • Chapter 5: Managing Models with MLflow
  • Technical requirements
  • Understanding models in MLflow
  • Exploring model flavors in MLflow
  • Custom models
  • Managing model signatures and schemas
  • Introducing Model Registry
  • Adding your best model to Model Registry
  • Managing the model development life cycle
  • Summary
  • Further reading
  • Section 3: Machine Learning in Production
  • Chapter 6: Introducing ML Systems Architecture
  • Technical requirements
  • Understanding challenges with ML systems and projects
  • Surveying state-of-the-art ML platforms
  • Getting to know Michelangelo
  • Getting to know Kubeflow
  • Architecting the PsyStock ML platform
  • Describing the features of the ML platform
  • High-level systems architecture
  • MLflow and other ecosystem tools
  • Summary
  • Further reading
  • Chapter 7: Data and Feature Management
  • Technical requirements
  • Structuring your data pipeline project
  • Acquiring stock data
  • Checking data quality

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