Book Image

Machine Learning Engineering with MLflow

By : Natu Lauchande
2 (1)
Book Image

Machine Learning Engineering with MLflow

2 (1)
By: Natu Lauchande

Overview of this book

MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you’ll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Problem Framing and Introductions
Section 1: Problem Framing and Introductions
Free Chapter
2
Chapter 1: Introducing MLflow
Chapter 1: Introducing MLflow
Technical requirements
What is MLflow?
Getting started with MLflow
Exploring MLflow modules
Summary
Further reading
3
Chapter 2: Your Machine Learning Project
Chapter 2: Your Machine Learning Project
Technical requirements
Exploring the machine learning process
Framing the machine learning problem
Introducing the stock market prediction problem
Sentiment analysis of market influencers
Developing your machine learning baseline pipeline
Summary
Further reading
4
Section 2: Model Development and Experimentation
Section 2: Model Development and Experimentation
5
Chapter 3: Your Data Science Workbench
Chapter 3: Your Data Science Workbench
Technical requirements
Understanding the value of a data science workbench
Creating your own data science workbench
Using the workbench for stock prediction
Summary
Further reading
6
Chapter 4: Experiment Management in MLflow
Chapter 4: Experiment Management in MLflow
Technical requirements
Getting started with the experiments module
Defining the experiment
Adding experiments
Comparing different models
Tuning your model with hyperparameter optimization
Summary
Further reading
7
Chapter 5: Managing Models with MLflow
Chapter 5: Managing Models with MLflow
Technical requirements
Understanding models in MLflow
Exploring model flavors in MLflow
Managing model signatures and schemas
Introducing Model Registry
Managing the model development life cycle
Summary
Further reading
8
Section 3: Machine Learning in Production
Section 3: Machine Learning in Production
9
Chapter 6: Introducing ML Systems Architecture
Chapter 6: Introducing ML Systems Architecture
Technical requirements
Understanding challenges with ML systems and projects
Surveying state-of-the-art ML platforms
Architecting the PsyStock ML platform
Summary
Further reading
10
Chapter 7: Data and Feature Management
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
Using a feature store
Summary
Further reading
11
Chapter 8: Training Models with MLflow
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
12
Chapter 9: Deployment and Inference with MLflow
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
13
Section 4: Advanced Topics
Section 4: Advanced Topics
14
Chapter 10: Scaling Up Your Machine Learning Workflow
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
15
Chapter 11: Performance Monitoring
Chapter 11: Performance Monitoring
Technical requirements
Overview of performance monitoring for machine learning models
Monitoring data drift and model performance
Infrastructure monitoring and alerting
Summary
Further reading
16
Chapter 12: Advanced Topics with MLflow
Chapter 12: Advanced Topics with MLflow
Technical requirements
Exploring MLflow use cases with AutoML
Integrating MLflow with other languages
Understanding MLflow plugins
Summary
Further reading
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17
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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The model.pkl file contains a serialized version of the model"

A block of code is set as follows:

import mlflow
from sklearn.linear_model import LogisticRegression
mlflow.sklearn.autolog()
with mlflow.start_run():
    clf = LogisticRegression()
    clf.fit(X_train, y_train)

Any command-line input or output is written as follows:

docker build -t stockpred -f dockerfile

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "MLflow is an open source platform for the machine learning (ML) life cycle"

Tips or important notes

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