Yes. As machines are getting cheaper and Data scientists are getting more expensive and hard to find, it is always better to use no code tools for ML development.

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The idea is not the low code itself, but how to find the best model and deploy it and monitor it. I would argue that this is done via experimentation, and this could be automated.

If we define the ML life cycle as:

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1. Understand the business problem, Map the business problem to ML
2. Get the data
3. Prepare the data
4. Feature engineering
5. Feature selection
6. Model training (train, test, optimize)
7. Model deployment
8. Model monitoring.

There are tools today that can do 3,4,5,6,7,8 automatically. In some cases (e.g. 1000 models) a data scientist simply does not have time to code the models. Also, imagine that you have only 10 models, but get new training data every week, are you going to code new models every week?

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The reasons that these tools are not in wider use are:

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1. AI / ML is not widespread (outside of tech)
2. The tools are (very) expensive.
3. The tools mainly focus on training and lack deployment and monitoring.(at least the open source one).

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At the end of the day, businesses care about productivity, I.e. can you get as many models into production in the short amount of time possible.