Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow

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
	Generating a feature set and training data
		Running your end-to-end pipeline
	Using a feature store
	Summary
	Further reading
Chapter 8: Training Models with MLflow
	Technical requirements
	Creating your training project with MLflow
	Implementing the training job
	Evaluating the model
	Deploying the model in the Model Registry
	Creating a Docker image for your training job
	Summary
	Further reading
Chapter 9: Deployment and Inference with MLflow
	Technical requirements
	Starting up a local model registry
	Setting up a batch inference job
	Creating an API process for inference
	Deploying your models for batch scoring in Kubernetes
	Making a cloud deployment with AWS SageMaker
	Summary
	Further reading
Section 4: Advanced Topics
Chapter 10: Scaling Up Your Machine Learning Workflow
	Technical requirements
	Developing models with a Databricks Community Edition environment
	Integrating MLflow with Apache Spark
	Integrating MLflow with NVIDIA RAPIDS (GPU)
	Integrating MLflow with the Ray platform
	Summary
	Further reading
Chapter 11: Performance Monitoring
	Technical requirements
	Overview of performance monitoring for machine learning models
	Monitoring data drift and model performance
		Monitoring data drift
		Monitoring target drift
		Monitoring model drift
	Infrastructure monitoring and alerting
	Summary
	Further reading
Chapter 12: Advanced Topics with MLflow
	Technical requirements
	Exploring MLflow use cases with AutoML
		AutoML pyStock classification use case
		AutoML – anomaly detection in fraud
	Intergrating MLflow with other languages
		MLflow Java example
		MLflow R example
	Understanding MLflow plugins
	Summary
	Further reading
Index