In practice, MLOps and DevOps often work together in organizations that develop and deploy machine learning applications. MLOps can be seen as an extension of DevOps principles tailored for machine learning projects, incorporating additional considerations specific to ML workflows. By leveraging the best practices from both MLOps and DevOps, organizations can build robust, scalable, and maintainable machine learning systems.
At last a good explained tech talk!! thanks..
Very clear and crisp explanation , thankyou !
We are glad to hear it was helpful for you. 🙌
In practice, MLOps and DevOps often work together in organizations that develop and deploy machine learning applications. MLOps can be seen as an extension of DevOps principles tailored for machine learning projects, incorporating additional considerations specific to ML workflows. By leveraging the best practices from both MLOps and DevOps, organizations can build robust, scalable, and maintainable machine learning systems.
100% correct. Since ML was used extensively, the ML domain has introduced dedicated MLOps 🙂
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