What does an MLOps Engineer do
What is MLOps. What does the role look like. What you need to know to get a job in this field.
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AI is the goal and ML is a vehicle to get there. When you build cloud-based applications, DevOps plays an important role in making sure that application runs well in the real world. MLOps plays the same role for AI applications.
If you're doing an academic project or building a toy application, MLOps might not be relevant to you just yet. But when you're deploying it in production for real paying customers, it becomes very relevant. How exactly does MLOps matter to your AI product? What does the day-to-day look like for an MLOps Engineer?
I've outlined 9 key responsibilities of an MLOps Engineer here:
1. Collecting the data
The MLOps Engineer needs to gather the data from multiple sources.
Everything related to data costs money in the cloud.
Be it storing, moving, or retrieving. So it's important that they do this efficiently. They need to make sure there's continuity in the data operations. And they need to check it periodically to ensure that the connections are active.
2. Validating the data
Once the data is collected, the next step is to validate the data quality. The MLOps Engineer needs to make sure that the data quality meets the standard. They need to look for anomalies and flag them.
3. Transforming the data
Once the data is validated, the MLOps Engineer needs to centralize and transform all of it. They need to preprocess it as needed. The goal is to make sure that the data is in the required format for further processing.
4. Labeling the data
Once the data is in the required format, they need to get it labeled. They need to store the labeled data is an efficient format.
They will have to ensure that this labeled data can be used to train the ML model.
5. Training the model
They need to train the model using the labeled data. They will have to check if it's giving out the required answer. They will have to generate the model file.
6. Validating the model
They should measure the performance of the AI model. They need to check if the model is functioning properly for a variety of inputs. They should test it on data that the model hasn't seen before.
7. Deploying the model
Once the model is validated, they need to put into production. The model should be ready to make inferences in the real world as new data comes in.
The performance of the product should remain steady as the volume of data increases.
8. Monitoring the model
The MLOps Engineer needs to keep an eye on the accuracy of the model. The models tend to become stale as time goes by. They need to keep track of the drift in performance. And automate the process of notifying the right people within the company.
9. Retraining the model
As new data comes in, your model needs to account for the new reality.
Periodic retraining of the model is vital to building a robust AI product.
The MLOps Engineer will have automate the process of retraining the model using expanded dataset.
Where to go from here
An MLOps Engineer builds and maintains the underlying infrastructure for AI applications. To make an AI application work well, you need robust infrastructure that can handle data operations. Big tech companies employ armies of MLOps Engineers to make sure their products run smoothly. You need to be knowledgeable about many aspects to be a good MLOps Engineer, which means that you can command a premium salary in the job market. It positions you well to build a product that people would want to use.