Do you know about MLops ,Yeah you got it correct NOT DevOps it’s MLops

AI systems don’t just take time to develop.

BUT  need to be actively maintained and monitored.

Let’s break down what the setup and ongoing costs of an AI solution looks like.

In the AI world. We typically divide model development into two phases,

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Model training and Deployment:

Model training involves researching the problem, gathering the appropriate data, selecting the right model type, and training it.

This journey is typically challenging as it exposes some organizational weaknesses, like which concrete business problems AI can solve, or messy data.

These problems can be solved, but are usually ignored in the initial assessment. Model training usually requires a significant amount of R&D from a business standpoint, and also a technological one. From a finance perspective, we can classify this as a capital expense.

Rather than training your own model, you can also purchase or license models. After a model is trained, it needs to be Deployed, and integrated with existing systems.


This might also require significant innovation depending on your business problem. Privacy, latency, throughput, and network connectivity would all affect the deployment pattern that you choose in the product configurations you would have. Once the models and their infrastructure are up and running,

In most modern applications, you’ll need to continuously monitor and update your models, which is typically referred to as MLOps. Like software, models aren’t static, and need to be updated and retrained. So these are the key considerations for model setup and ongoing expenses. In the next video, we’ll discuss selecting the right model for the task.