About Build MLOps
Bridging the gap between machine learning research and production systems
Our Mission
Build MLOps is dedicated to helping data scientists, ML engineers, and DevOps professionals master the art and science of deploying machine learning systems in production. We believe that the gap between developing ML models and deploying them reliably at scale is one of the biggest challenges facing the industry today.
Through in-depth tutorials, real-world case studies, and practical guides, we aim to democratize MLOps knowledge and empower teams to build robust, scalable, and maintainable ML systems.
About the Author
Charles OBrien
Senior ML Engineer & Technical Writer
With over a decade of experience in software engineering and machine learning, I have worked on ML systems at scale across various industries including finance, healthcare, and technology. My passion lies in making complex ML concepts accessible and helping teams avoid the pitfalls I have encountered along the way.
I specialize in building end-to-end ML platforms, implementing robust CI/CD pipelines for ML, and designing monitoring systems that catch model drift before it impacts users. When I am not writing about MLOps, I am contributing to open-source projects or speaking at conferences about production ML.
What You Will Find Here
In-Depth Tutorials
Step-by-step guides covering everything from basic MLOps concepts to advanced deployment strategies, with real code examples and best practices.
Case Studies
Real-world examples of MLOps implementations, including challenges faced, solutions implemented, and lessons learned.
Best Practices
Industry-proven patterns and practices for building reliable, scalable, and maintainable ML systems in production.
Tool Reviews
Honest evaluations of MLOps tools and platforms, helping you choose the right stack for your needs.
Technologies We Cover
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