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What are the pros and cons of MLOps?

In the race to solve business problems, more companies have invested considerable capital into becoming data-driven. The hiring cycle is beginning to prioritize data scientists, data engineers, and DevOps engineers. What glues this team and the ML solutions pipeline together? MLOps platforms.

Pros: Remove silos with collaborative spaces and feature sharing, scale with continuous deployment, monitor for drift at scale

Once the data team is complete and some models are trained, companies begin to sense a problem. Silos between data scientists, engineers, and DevOps friction the path to deployment. The path to production is long and requires patience and stamina. As a result, valuable models get stuck in production.

MLOps platforms like Iguazio allow for collaboration through their UI and feature store, continuous integration and deployment, and model monitoring.

Cons: Initial costs

The only aspect that MLOps could be considered a “con” is the costs outside of long-term cost-benefit analysis. That is, introducing MLOps to your firm might be expensive if you think short-term. However, if you project the benefits of driving your decisions with data, then the only thing you should consider is whether you build or buy your MLOps solution.

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