August 2026
Intermediate to advanced
386 pages
10h 44m
English
Content preview from MLOps with Databricks
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Epilogue
Congratulations on finishing this book.
Writing it was far more challenging than I ever anticipated, especially because it covers a space that evolves at an extraordinary pace. Throughout the process, I had to significantly update the code multiple times (particularly the LLMOps chapters), while Databricks continuously introduced new features, renamed components, and improved abstractions. At times, it felt like trying to document a moving target.
And yet, the core principles remain stable.
Versioning data and models, ensuring reproducibility, applying guardrails, monitoring systems, and maintaining governance are not tied to specific tools. They reflect the fundamentals of building reliable and scalable AI systems, and they are not going away.
What is changing is how we build. We no longer build solutions to last; we build solutions to adapt.
AI is now used to build AI. Engineers rely on agents to generate code, while the platforms they use evolve just as quickly. This creates a new reality: features change, better abstractions emerge, and what was considered best practice just months ago may already be outdated.
Rather than chasing every new trend, focus on strong engineering practices. They provide the stability needed to build robust, compliant, and sustainable systems.
Ultimately, success starts with understanding what “good” looks like. I hope this book has given you that foundation.
Feel free to reach out:
- LinkedIn: linkedin.com/in/maria-vechtomova
- Email: vechtomova.maria@gmail.com ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.