CHAPTER 3Embracing MLOps

As the key factor behind succeeding with operating AI is embracing an operational mindset from start, the key to do that is to embrace the concept of MLOps (machine learning operations). In ML development the problem has never been to technically develop, train, or implement ML models; instead, the main problem is mostly related to poor communication and lack of efficient cross-functional team collaboration. It might sound like an easy task to correct, but the fact remains that most AI projects do not make it to production due to this communication gap between the data scientists and the business. And that's where MLOps can help.

MLOps refers to best practices of operationalizing ML models. This approach is all about bringing data scientists and engineers closer together through improved collaboration to achieve speed and robustness throughout the ML life cycle. A key success factor for MLOps practices is also the emphasis on continuous feedback from business stakeholders.

The main challenge that MLOps is trying to solve is building an integrated AI system for continuous operations in the production environment, without any major disconnects.

By adopting sustainable MLOps best practices, you will gain:

  • Improved visibility providing end-to-end visibility of data extraction, model creation, deployment, and monitoring for faster processing
  • Faster auditing, including improved ability to replicate production models by storing all related artifacts such ...

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