Questions tagged [deployment]
The deployment tag has no usage guidance.
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Deploying a model with GPU and pay-per-inference
I may have the wrong stack exchange. If that's the case, could someone point me to a stack that could help with this. Anyways...
My backend employs a sentence transformer model from HuggingFace. Since ...
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How to deploy a machine learning model in the cloud with API access?
I want to deploy a machine learning model (more precisely stable diffusion) in the cloud and make it accessible via an API. I am relatively new to this topic, so I am not quite sure what services ...
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Do model training pipeline should run on dev, staging and production environment?
I know it's a best practice to ship our code from dev to staging to production by including different level tests and validations that will help to confidently deploy on the production environment.
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Ways to share Pytorch model without revealing architecture?
We are trying to give a model to collaborators but would like to protect the IP. What are some ways to encrypt/hide/compile the definition when sharing a trained model?
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What is the best approach to deploy N number of ML models as a scalable service in the Cloud?
I've N (~50) number of sentiment models of different languages, which were fine tuned on HggingFace's transformer models. Each of the models as 2-3 GB in size approx. Now, how can I deploy all these ...
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Can accuracy improve when there is evidence of domain-shift between training and deployment?
A model for image analysis was trained using data captured with imaging system A. I then deployed the model on imaging system B. System B has better image contrast than system A. Features in the last ...
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How to use early_stopping_rounds in the Final Model? (CatBoost example with Optuna)
Imagine we have a model in the sklearn pipeline:
...
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Which random_state to use in test_train_split when deploying final model?
I have developed a Random Forest that gives varying results depending on the random state of the test train split. This is normal, because a lot of the values in the data are extreme, without being ...
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After experimentation, how do we learn a final model for deployment?
I have a question regarding learning a "final" model for deployment. Assuming my task is a classification one, my workflow during experimentation is as follows.
Get the data: ...
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ValueError: dtype='numeric' is not compatible with arrays of bytes/strings.Convert your data to numeric values explicitly instead
I am new to machine learning model deployment. In a sequence, local system rises an error while running "app.py" file, that is:
...
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Can I use Population Stability Index (PSI) when observations have multiple variables?
I understand from resources like this one that the Population Stability Index (PSI) can be used to test for data drift when a machine learning model is in production. However, the resources I have ...
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How is model scheduling set up in practice?
I have been working on various machine learning models so far, but never yet on the deployment phase of an ML project. I have vaguely used Apache Airflow and I'm aware that it is a tool for scheduling ...
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FileNotFoundError: Unsuccessful TensorSliceReader constructor
I am trying to deploy my model. I am encountering the following problem:
FileNotFoundError: Unsuccessful TensorSliceReader constructor: Failed
to find any matching files for
ram://a603e930-4fda-4105-...
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How to handle categorical feature engineering in ML production?
I have a classification dataset ,where I have a lot of categorical columns .
I have one hot encoded ie. dummy variables in my training .
How to handle this in production side of ML. There are cases ...
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