Practitioners guide to MLOps

Comprehensive whitepaper on MLOps best practices and strategies for effective deployment and management of ML systems in organizations.

Introduction

The Practitioners Guide to MLOps is a comprehensive whitepaper that delves into the integration of machine learning and operations, providing insights into MLOps best practices and strategies. The authors, Khalid Samala, Jarek Kazmierczak, and Donna Schut, offer practical guidance for implementing MLOps in real-world scenarios, making this whitepaper a valuable resource for professionals in the field of data science, machine learning, and DevOps.

Highlights

• Provides a detailed overview of the MLOps lifecycle and core capabilities
• Offers a deep dive into the key MLOps processes, including experimentation, data processing, model training, and model deployment
• Covers essential MLOps capabilities such as ML pipelines, model registry, dataset and feature management, and model governance
• Aims to help organizations improve collaboration, reliability, scalability, and development cycle times for ML systems

Recommendation

This whitepaper is recommended for technology leaders, enterprise architects, and teams who want to understand and implement MLOps practices to effectively build, deploy, and operate machine learning systems in their organizations.