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Top MLOps Books to Read in 2024
Machine Learning Design Patterns
Covers common problems in machine learning and their solutions. Teaches building robust training loops and deploying scalable ML systems.
Introducing MLOps
Introduces the fundamentals of MLOps to help operationalize machine learning models. Teaches designing unbiased, fair, and explainable MLOps life cycles.
Designing Machine Learning Systems
Teaches designing reliable and scalable machine-learning systems using case studies. Provides a guide on automating the process, developing a monitoring system, and responsible ML systems.
Machine Learning Engineering
Covers machine learning engineering best practices and design patterns. Focuses on building and deploying ML solutions.
Machine Learning Engineering with Python
A practical guide to building scalable solutions using Python. Covers latest tools and frameworks, including Generative AI and LangChain.
Reliable Machine Learning
Provides a guide on running and establishing ML models reliably, effectively, and accountably. Demonstrates applying the SRE mindset to machine learning.
Building Machine Learning Pipelines
Covers automating model life cycles with TensorFlow and orchestrating pipelines with Apache Beam, Apache Airflow, and Kubeflow Pipelines. Sheds light on data validation, model monitoring, and model quantization.
Practical MLOps
Teaches building production-grade machine learning systems and choosing the correct MLOps tools. Covers implementing solutions in cloud platforms like AWS, Microsoft Azure, and Google Cloud.
Machine Learning in Production
A comprehensive guide to managing the lifecycle of a machine learning project, from development to deployment. Covers topics like CI/CD, managing the ML life cycle, and deployment on cloud platforms.
Implementing MLOps in the Enterprise
Helps organizations tackle challenges in moving ML models to production. Teaches designing continuous operational pipelines.
Engineering MLOps
Covers various MLOps techniques to build and manage scalable ML life cycles. Provides real-world examples in Azure to deploy models securely in production.
Managing Data Science
Helps managers understand data science concepts and methodologies to tackle data science challenges.
Machine Learning Engineering in Action
Consists of tricks and design patterns for developing scalable and secure ML models. Guides in choosing the right technologies and tools and automating troubleshooting and logging practices.
Building Machine Learning Powered Applications
Teaches necessary skills to design, build, and deploy ML-powered applications. Readers also get to build an example ML-driven application from scratch.
Machine Learning Engineering on AWS
Covers AWS services for creating scalable and secure ML systems and MLOps pipelines, including AWS SageMaker, AWS EKS, AWS Lambda, etc.
Data Science Solutions on Azure
A guide on using Microsoft Azure tools to develop data-driven solutions. Ideal for data scientists deploying ML solutions on Azure.
Continuous Machine Learning with Kubeflow
Provides knowledge of deploying ML pipelines using Docker and Kubernetes. Explains deploying ML applications with TensorFlow training and serving with Kubernetes.
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